Arithmetic Optimization with Ensemble Deep Transfer Learning BasedMelanoma Classification

Melanoma is a skin disease with high mortality rate while early diagnoses of the disease can increase the survival chances of patients. It is challenging to automatically diagnose melanoma from dermoscopic skin samples. Computer-Aided Diagnostic (CAD) tool saves time and effort in diagnosing melanoma compared to existing medical approaches. In this background, there is a need exists to design an automated classification model for melanoma that can utilize deep and rich feature datasets of an image for disease classification. The current study develops an Intelligent Arithmetic Optimization with Ensemble Deep Transfer Learning Based Melanoma Classification (IAOEDTT-MC) model. The proposed IAOEDTT-MC model focuses on identification and classification of melanoma from dermoscopic images. To accomplish this, IAOEDTT-MC model applies image preprocessing at the initial stage in which Gabor Filtering (GF) technique is utilized. In addition, U-Net segmentation approach is employed to segment the lesion regions in dermoscopic images. Besides, an ensemble of DL models including ResNet50 and ElasticNet models is applied in this study. Moreover, AO algorithm with Gated Recurrent Unit (GRU) method is utilized for identification and classification of melanoma. The proposed IAOEDTT-MC method was experimentally validated with the help of benchmark datasets and the proposed model attained maximum accuracy of 92.09% on ISIC 2017 dataset.     

Handling dataset dependence with model ensembles for skin lesion classification from dermoscopic and clinical images

Dataset dependence affects many real-life applications of machine learning: the performance of a model trained on a dataset is significantly worse on samples from another dataset than on new, unseen samples from the original one. This issue is particularly acute for small and somewhat specific databases in medical applications; the automated recognition of melanoma from skin lesion images is a prime example. We document dataset dependence in dermoscopic skin lesion image classification using three publicly available medium size datasets. Standard machine learning techniques aimed at improving the predictive power of a model might enhance performance slightly, but the gain is small, the dataset dependence is not reduced, and the best combination depends on model details. We demonstrate that simple differences in image statistics account for only 5% of the dataset dependence. We suggest a solution with two essential ingredients: using an ensemble of heterogeneous models, and training on a heterogeneous dataset. Our ensemble consists of 29 convolutional networks, some of which are trained on features considered important by dermatologists; the networks' output is fused by a trained committee machine. The combined International Skin Imaging Collaboration dataset is suitable for training, as it is multi-source, produced by a collaboration of a number of clinics over the world. Building on the strengths of the ensemble, it is applied to a related problem as well: recognizing melanoma based on clinical (non-dermoscopic) images. This is a harder problem as both the image quality is lower than those of the dermoscopic ones and the available public datasets are smaller and scarcer. We explored various training strategies and showed that 79% balanced accuracy can be achieved for binary classification averaged over three clinical datasets.     

Optimal Deep Transfer Learning Model for Histopathological Breast CancerClassification

Earlier recognition of breast cancer is crucial to decrease the severity and optimize the survival rate. One of the commonly utilized imaging modalities for breast cancer is histopathological images. Since manual inspection of histopathological images is a challenging task, automated tools using deep learning (DL) and artificial intelligence (AI) approaches need to be designed. The latest advances of DL models help in accomplishing maximum image classification performance in several application areas. In this view, this study develops a Deep Transfer Learning with Rider Optimization Algorithm for Histopathological Classification of Breast Cancer (DTLRO-HCBC) technique. The proposed DTLRO-HCBC technique aims to categorize the existence of breast cancer using histopathological images. To accomplish this, the DTLRO-HCBC technique undergoes pre-processing and data augmentation to increase quantitative analysis. Then, optimal SqueezeNet model is employed for feature extractor and the hyperparameter tuning process is carried out using the Adadelta optimizer. Finally, rider optimization with deep feed forward neural network (RO-DFFNN) technique was utilized employed for breast cancer classification. The RO algorithm is applied for optimally adjusting the weight and bias values of the DFFNN technique. For demonstrating the greater performance of the DTLRO-HCBC approach, a sequence of simulations were carried out and the outcomes reported its promising performance over the current state of art approaches.     

Fitness adaptive deer hunting-based region growing and recurrent neural network for melanoma skin cancer detection

This proposal aims to enhance the accuracy of a dermoscopic skin cancer diagnosis with the aid of novel deep learning architecture. The proposed skin cancer detection model involves four main steps: (a) preprocessing, (b) segmentation, (c) feature extraction, and (d) classification. The dermoscopic images initially subjected to a preprocessing step that includes image enhancement and hair removal. After preprocessing, the segmentation of lesion is deployed by an optimized region growing algorithm. In the feature extraction phase, local features, color morphology features, and morphological transformation-based features are extracted. Moreover, the classification phase uses a modified deep learning algorithm by merging the optimization concept into recurrent neural network (RNN). As the main contribution, the region growing and RNN improved by the modified deer hunting optimization algorithm (DHOA) termed as Fitness Adaptive DHOA (FA-DHOA). Finally, the analysis has been performed to verify the effectiveness of the proposed method.     

Diagnosis of Various Skin Cancer Lesions Based on Fine-Tuned ResNet50 Deep Network

With the massive success of deep networks, there have been significant efforts to analyze cancer diseases, especially skin cancer. For this purpose, this work investigates the capability of deep networks in diagnosing a variety of dermoscopic lesion images. This paper aims to develop and fine-tune a deep learning architecture to diagnose different skin cancer grades based on dermatoscopic images. Fine-tuning is a powerful method to obtain enhanced classification results by the customized pre-trained network. Regularization, batch normalization, and hyperparameter optimization are performed for fine-tuning the proposed deep network. The proposed fine-tuned ResNet50 model successfully classified 7-respective classes of dermoscopic lesions using the publicly available HAM10000 dataset. The developed deep model was compared against two powerful models, i.e., InceptionV3 and VGG16, using the Dice similarity coefficient (DSC) and the area under the curve (AUC). The evaluation results show that the proposed model achieved higher results than some recent and robust models.     

Melanoma Identification Through X-ray Modality Using Inception-v3 Based Convolutional Neural Network

Melanoma, also called malignant melanoma, is a form of skin cancer triggered by an abnormal proliferation of the pigment-producing cells, which give the skin its color. Melanoma is one of the skin diseases, which is exceptionally and globally dangerous, Skin lesions are considered to be a serious disease. Dermoscopy-based early recognition and detection procedure is fundamental for melanoma treatment. Early detection of melanoma using dermoscopy images improves survival rates significantly. At the same time, well-experienced dermatologists dominate the precision of diagnosis. However, precise melanoma recognition is incredibly hard due to several factors: low contrast between lesions and surrounding skin, visual similarity between melanoma and non-melanoma lesions, and so on. Thus, reliable automatic detection of skin tumors is critical for pathologists’ effectiveness and precision. To take care of this issue, numerous research centers around the world are creating autonomous image processing-oriented frameworks. We suggested deep learning methods in this article to address significant tasks that have emerged in the field of skin lesion image processing: we provided a Convolutional Neural Network (CNN) based framework using an Inception-v3 (INCP-v3) melanoma detection scheme and accomplished very high precision (98.96%) against melanoma detection. The classification framework of CNN is created utilizing TensorFlow and Keras in the backend (in Python). It likewise utilizes Transfer-Learning (TL) approach. It is prepared on the data gathered from the “International Skin Imaging Collaboration (ISIC)” repositories. The experiments show that the suggested technique outperforms state-of-the-art methods in terms of predictive performance.     

Hybrid Metaheuristics with Deep Learning Enabled Automated Deception Detection and Classification of Facial Expressions

Automatic deception recognition has received considerable attention from the machine learning community due to recent research on its vast application to social media, interviews, law enforcement, and the military. Video analysis-based techniques for automated deception detection have received increasing interest. This study develops a new self-adaptive population-based firefly algorithm with a deep learning-enabled automated deception detection (SAPFF-DLADD) model for analyzing facial cues. Initially, the input video is separated into a set of video frames. Then, the SAPFF-DLADD model applies the MobileNet-based feature extractor to produce a useful set of features. The long short-term memory (LSTM) model is exploited for deception detection and classification. In the final stage, the SAPFF technique is applied to optimally alter the hyperparameter values of the LSTM model, showing the novelty of the work. The experimental validation of the SAPFF-DLADD model is tested using the Miami University Deception Detection Database (MU3D), a database comprised of two classes, namely, truth and deception. An extensive comparative analysis reported a better performance of the SAPFF-DLADD model compared to recent approaches, with a higher accuracy of 99%.     

Deer Hunting Optimization with Deep Learning Model for Lung Cancer Classification

Lung cancer is the main cause of cancer related death owing to its destructive nature and postponed detection at advanced stages. Early recognition of lung cancer is essential to increase the survival rate of persons and it remains a crucial problem in the healthcare sector. Computer aided diagnosis (CAD) models can be designed to effectually identify and classify the existence of lung cancer using medical images. The recently developed deep learning (DL) models find a way for accurate lung nodule classification process. Therefore, this article presents a deer hunting optimization with deep convolutional neural network for lung cancer detection and classification (DHODCNN-LCC) model. The proposed DHODCNN-LCC technique initially undergoes pre-processing in two stages namely contrast enhancement and noise removal. Besides, the features extraction process on the pre-processed images takes place using the Nadam optimizer with RefineDet model. In addition, denoising stacked autoencoder (DSAE) model is employed for lung nodule classification. Finally, the deer hunting optimization algorithm (DHOA) is utilized for optimal hyper parameter tuning of the DSAE model and thereby results in improved classification performance. The experimental validation of the DHODCNN-LCC technique was implemented against benchmark dataset and the outcomes are assessed under various aspects. The experimental outcomes reported the superior outcomes of the DHODCNN-LCC technique over the recent approaches with respect to distinct measures.     

Kernel Granulometric Texture Analysis and Light RES-ASPP-UNET Classification for Covid-19 Detection

This research article proposes an automatic frame work for detecting COVID -19 at the early stage using chest X-ray image. It is an undeniable fact that coronovirus is a serious disease but the early detection of the virus present in human bodies can save lives. In recent times, there are so many research solutions that have been presented for early detection, but there is still a lack in need of right and even rich technology for its early detection. The proposed deep learning model analysis the pixels of every image and adjudges the presence of virus. The classifier is designed in such a way so that, it automatically detects the virus present in lungs using chest image. This approach uses an image texture analysis technique called granulometric mathematical model. Selected features are heuristically processed for optimization using novel multi scaling deep learning called light weight residual–atrous spatial pyramid pooling (LightRES-ASPP-Unet) Unet model. The proposed deep LightRES-ASPP-Unet technique has a higher level of contracting solution by extracting major level of image features. Moreover, the corona virus has been detected using high resolution output. In the framework, atrous spatial pyramid pooling (ASPP) method is employed at its bottom level for incorporating the deep multi scale features in to the discriminative mode. The architectural working starts from the selecting the features from the image using granulometric mathematical model and the selected features are optimized using LightRES-ASPP-Unet. ASPP in the analysis of images has performed better than the existing Unet model. The proposed algorithm has achieved 99.6% of accuracy in detecting the virus at its early stage.     

Compact Bat Algorithm with Deep Learning Model for Biomedical EEG EyeState Classification

Electroencephalography (EEG) eye state classification becomes an essential tool to identify the cognitive state of humans. It can be used in several fields such as motor imagery recognition, drug effect detection, emotion categorization, seizure detection, etc. With the latest advances in deep learning (DL) models, it is possible to design an accurate and prompt EEG EyeState classification problem. In this view, this study presents a novel compact bat algorithm with deep learning model for biomedical EEG EyeState classification (CBADL-BEESC) model. The major intention of the CBADL-BEESC technique aims to categorize the presence of EEG EyeState. The CBADL-BEESC model performs feature extraction using the ALexNet model which helps to produce useful feature vectors. In addition, extreme learning machine autoencoder (ELM-AE) model is applied to classify the EEG signals and the parameter tuning of the ELM-AE model is performed using CBA. The experimental result analysis of the CBADL-BEESC model is carried out on benchmark results and the comparative outcome reported the supremacy of the CBADL-BEESC model over the recent methods.     

Automated Artificial Intelligence Empowered White Blood Cells ClassificationModel

White blood cells (WBC) or leukocytes are a vital component of the blood which forms the immune system, which is accountable to fight foreign elements. The WBC images can be exposed to different data analysis approaches which categorize different kinds of WBC. Conventionally, laboratory tests are carried out to determine the kind of WBC which is erroneous and time consuming. Recently, deep learning (DL) models can be employed for automated investigation of WBC images in short duration. Therefore, this paper introduces an Aquila Optimizer with Transfer Learning based Automated White Blood Cells Classification (AOTL-WBCC) technique. The presented AOTL-WBCC model executes data normalization and data augmentation process (rotation and zooming) at the initial stage. In addition, the residual network (ResNet) approach was used for feature extraction in which the initial hyperparameter values of the ResNet model are tuned by the use of AO algorithm. Finally, Bayesian neural network (BNN) classification technique has been implied for the identification of WBC images into distinct classes. The experimental validation of the AOTL-WBCC methodology is performed with the help of Kaggle dataset. The experimental results found that the AOTL-WBCC model has outperformed other techniques which are based on image processing and manual feature engineering approaches under different dimensions.     

Performance enhanced PSO-based modified Kohonen neural network for retinal image classification

Image classification is one of the significant applications in the field of ophthalmology for abnormality detection in retinal images. Image classification is a pattern recognition technique in which abnormal retinal images are categorized into different groups based on similarity measures. Accuracy and convergence rate are the important parameters of this automated diagnostic system. Artificial neural networks (ANNs) are widely used for automated image analysis systems. Kohonen neural networks (KNNs) are one of the prime unsupervised ANNs suitable for image processing applications. Besides the numerous advantages, KNNs suffer from two drawbacks: (a) lack of standard convergence conditions and (b) less accurate results. In this study, a novel approach is adopted to eliminate these disadvantages by performing suitable modifications in the conventional KNN. Initially, the fuzzy approach is an integrated one within KNN in the training algorithm to overcome the convergence difficulties. Second, a particle swarm optimization algorithm is used in feature selection for better accuracy. This proposed approach is tested on four different abnormal retinal image categories. The system is analyzed using several performance measures and the experimental results suggest promising results for the proposed system. Comparative analyses with other systems are also presented to show the superior nature of the proposed system.     

基于深度学习的烟包识别与分类

目的 提取烟包图像数据训练深度学习目标检测模型,提升烟包流水线拣包效率和准确性。方法 基于深度学习建立一种烟包识别分类模型,对原始YOLOv3模型进行改进,在原网络中加入设计的多空间金字塔池化结构(M–SPP),将64×64尺度的特征图下采样与32×32尺度的特征图进行拼接,并去除16×16尺度的预测特征层,提高模型的检测准确率和速度,并采用K–means++算法对先验框参数进行优化。结果 实验表明该目标检测模型平均准确率达到99.68%,检测速度达到70.82帧/s。结论 基于深度学习建立的图像识别分类模型准确率高且检测速度快,有效满足烟包流水线自动化实时检测。     

Automatic diagnosis of diabetic retinopathy with the aid of adaptive average filtering with optimized deep convolutional neural network

The most effective treatment for diabetic retinopathy (DR) is the early detection through regular screening, which is critical for a better prognosis. Automatic screening of the images would assist the physicians in diagnosing the condition of patients easily and accurately. This condition searches out for special importance of image processing technology in the way of processing the retinal fundus images. Accordingly, this article plans to develop an automatic DR detection model with the aid of three main stages like (a) image preprocessing, (b) blood vessel segmentation, and (c) classification. The preprocessing phase includes two steps: conversion of RGB to Lab, and contrast enhancement. The Histogram equalization process is done using the contrast enhancement of an image. To the next of preprocessing, the segmentation phase starts with a valuable procedure. It includes (a), thresholding the contrast-enhanced and filtered images, (b) thresholding the keypoints of contrast-enhanced and filtered images, and (c) adding both thresholded binary images. Here, the filtering process is performed by proposed adaptive average filtering, where the filter coefficients are tuned or optimized by an improved meta-heuristic algorithm called fitness probability-based CSO (FP-CSO). Finally, the classification part uses Deep CNN, where the improvement is exploited on the convolutional layer, which is optimized by the same improved FP-CSO. Since the conventional CSO depends on a fitness probability in the improved algorithm, the proposed algorithm termed as FP-CSO. Finally, valuable comparative and performance analysis has confirmed the effectiveness of the proposed model.     

Automated Deep Learning Empowered Breast Cancer Diagnosis Using Biomedical Mammogram Images

Biomedical image processing is a hot research topic which helps to majorly assist the disease diagnostic process. At the same time, breast cancer becomes the deadliest disease among women and can be detected by the use of different imaging techniques. Digital mammograms can be used for the earlier identification and diagnostic of breast cancer to minimize the death rate. But the proper identification of breast cancer has mainly relied on the mammography findings and results to increased false positives. For resolving the issues of false positives of breast cancer diagnosis, this paper presents an automated deep learning based breast cancer diagnosis (ADL-BCD) model using digital mammograms. The goal of the ADL-BCD technique is to properly detect the existence of breast lesions using digital mammograms. The proposed model involves Gaussian filter based pre-processing and Tsallis entropy based image segmentation. In addition, Deep Convolutional Neural Network based Residual Network (ResNet 34) is applied for feature extraction purposes. Specifically, a hyper parameter tuning process using chimp optimization algorithm (COA) is applied to tune the parameters involved in ResNet 34 model. The wavelet neural network (WNN) is used for the classification of digital mammograms for the detection of breast cancer. The ADL-BCD method is evaluated using a benchmark dataset and the results are analyzed under several performance measures. The simulation outcome indicated that the ADL-BCD model outperforms the state of art methods in terms of different measures.     

Brain Tumor Detection and Classification Using PSO and Convolutional Neural Network

Tumor detection has been an active research topic in recent years due to the high mortality rate. Computer vision (CV) and image processing techniques have recently become popular for detecting tumors in MRI images. The automated detection process is simpler and takes less time than manual processing. In addition, the difference in the expanding shape of brain tumor tissues complicates and complicates tumor detection for clinicians. We proposed a new framework for tumor detection as well as tumor classification into relevant categories in this paper. For tumor segmentation, the proposed framework employs the Particle Swarm Optimization (PSO) algorithm, and for classification, the convolutional neural network (CNN) algorithm. Popular preprocessing techniques such as noise removal, image sharpening, and skull stripping are used at the start of the segmentation process. Then, PSO-based segmentation is applied. In the classification step, two pre-trained CNN models, alexnet and inception-V3, are used and trained using transfer learning. Using a serial approach, features are extracted from both trained models and fused features for final classification. For classification, a variety of machine learning classifiers are used. Average dice values on datasets BRATS-2018 and BRATS-2017 are 98.11 percent and 98.25 percent, respectively, whereas average jaccard values are 96.30 percent and 96.57% (Segmentation Results). The results were extended on the same datasets for classification and achieved 99.0% accuracy, sensitivity of 0.99, specificity of 0.99, and precision of 0.99. Finally, the proposed method is compared to state-of-the-art existing methods and outperforms them.     

A Deep Learning Approach for Detecting Covid-19 Using the Chest X-RayImages

Real-time detection of Covid-19 has definitely been the most widely-used world-wide classification problem since the start of the pandemic from 2020 until now. In the meantime, airspace opacities spreads related to lung have been of the most challenging problems in this area. A common approach to do on that score has been using chest X-ray images to better diagnose positive Covid-19 cases. Similar to most other classification problems, machine learning-based approaches have been the first/most-used candidates in this application. Many schemes based on machine/deep learning have been proposed in recent years though increasing the performance and accuracy of the system has still remained an open issue. In this paper, we develop a novel deep learning architecture to better classify the Covid-19 X-ray images. To do so, we first propose a novel multi-habitat migration artificial bee colony (MHMABC) algorithm to improve the exploitation/exploration of artificial bee colony (ABC) algorithm. After that, we optimally train the fully connected by using the proposed MHMABC algorithm to obtain better accuracy and convergence rate while reducing the execution cost. Our experiment results on Covid-19 X-ray image dataset show that the proposed deep architecture has a great performance in different important optimization parameters. Furthermore, it will be shown that the MHMABC algorithm outperforms the state-of-the-art algorithms by evaluating its performance using some well-known benchmark datasets.     

An Optimized Approach to Vehicle-Type Classification Using a Convolutional Neural Network

Vehicle type classification is considered a central part of an intelligent traffic system. In recent years, deep learning had a vital role in object detection in many computer vision tasks. To learn high-level deep features and semantics, deep learning offers powerful tools to address problems in traditional architectures of handcrafted feature-extraction techniques. Unlike other algorithms using handcrated visual features, convolutional neural network is able to automatically learn good features of vehicle type classification. This study develops an optimized automatic surveillance and auditing system to detect and classify vehicles of different categories. Transfer learning is used to quickly learn the features by recording a small number of training images from vehicle frontal view images. The proposed system employs extensive data-augmentation techniques for effective training while avoiding the problem of data shortage. In order to capture rich and discriminative information of vehicles, the convolutional neural network is fine-tuned for the classification of vehicle types using the augmented data. The network extracts the feature maps from the entire dataset and generates a label for each object (vehicle) in an image, which can help in vehicle-type detection and classification. Experimental results on a public dataset and our own dataset demonstrated that the proposed method is quite effective in detection and classification of different types of vehicles. The experimental results show that the proposed model achieves 96.04% accuracy on vehicle type classification.     

Dental Age Estimation Based on X-ray Images

Chronological age estimation using panoramic dental X-ray images is an essential task in forensic sciences. Various statistical approaches have proposed by considering the teeth and mandible. However, building automated dental age estimation based on machine learning techniques needs more research efforts. In this paper, an automated dental age estimation is proposed using transfer learning. In the proposed approach, features are extracted using two deep neural networks namely, AlexNet and ResNet. Several classifiers are proposed to perform the classification task including decision tree, k-nearest neighbor, linear discriminant, and support vector machine. The proposed approach is evaluated using a number of suitable performance metrics using a dataset that contains 1429 dental X-ray images. The obtained results show that the proposed approach has a promising performance.     

MI-STEG: A Medical Image Steganalysis Framework Based on Ensemble Deep Learning

Medical image steganography aims to increase data security by concealing patient-personal information as well as diagnostic and therapeutic data in the spatial or frequency domain of radiological images. On the other hand, the discipline of image steganalysis generally provides a classification based on whether an image has hidden data or not. Inspired by previous studies on image steganalysis, this study proposes a deep ensemble learning model for medical image steganalysis to detect malicious hidden data in medical images and develop medical image steganography methods aimed at securing personal information. With this purpose in mind, a dataset containing brain Magnetic Resonance (MR) images of healthy individuals and epileptic patients was built. Spatial Version of the Universal Wavelet Relative Distortion (S-UNIWARD), Highly Undetectable Stego (HUGO), and Minimizing the Power of Optimal Detector (MIPOD) techniques used in spatial image steganalysis were adapted to the problem, and various payloads of confidential data were hidden in medical images. The architectures of medical image steganalysis networks were transferred separately from eleven Dense Convolutional Network (DenseNet), Residual Neural Network (ResNet), and Inception-based models. The steganalysis outputs of these networks were determined by assembling models separately for each spatial embedding method with different payload ratios. The study demonstrated the success of pre-trained ResNet, DenseNet, and Inception models in the cover-stego mismatch scenario for each hiding technique with different payloads. Due to the high detection accuracy achieved, the proposed model has the potential to lead to the development of novel medical image steganography algorithms that existing deep learning-based steganalysis methods cannot detect. The experiments and the evaluations clearly proved this attempt.