共查询到20条相似文献，搜索用时 0 毫秒
1.
Bhaswati Singha Deo;Mayukha Pal;Prasanta K. Panigrahi;Asima Pradhan; 《International journal of imaging systems and technology》2024,34(2):e23043
Cervical cancer is one of the primary causes of death in women. It should be diagnosed early and treated according to the best medical advice, similar to other diseases, to ensure that its effects are as minimal as possible. Pap smear images are one of the most constructive ways for identifying this type of cancer. This study proposes a cross-attention-based Transfomer approach for the reliable classification of cervical cancer in pap smear images. In this study, we propose the CerviFormer-a model that depends on the Transformers and thereby requires minimal architectural assumptions about the size of the input data. The model uses a cross-attention technique to repeatedly consolidate the input data into a compact latent Transformer module, which enables it to manage very large-scale inputs. We evaluated our model on two publicly available pap smear datasets. For 3-state classification on the Sipakmed data, the model achieved an accuracy of 96.67%. For 2-state classification on the Herlev data, the model achieved an accuracy of 94.57%. Experimental results on two publicly accessible datasets demonstrate that the proposed method achieves competitive results when compared to contemporary approaches. The proposed method brings forth a comprehensive classification model to detect cervical cancer in pap smear images. This may aid medical professionals in providing better cervical cancer treatment, consequently, enhancing the overall effectiveness of the entire testing process. 相似文献
2.
XiaoQing Zhang Shu-Guang Zhao 《International journal of imaging systems and technology》2019,29(1):19-28
Cervical cancer is one of the most common gynecological malignancies, and when detected and treated at an early stage, the cure rate is almost 100%. Colposcopy can be used to diagnose cervical lesions by direct observation of the surface of the cervix using microscopic biopsy and pathological examination, which can improve the diagnosis rate and ensure that patients receive fast and effective treatment. Digital colposcopy and automatic image analysis can reduce the work burden on doctors, improve work efficiency, and help healthcare institutions to make better treatment decisions in underdeveloped areas. The present study used a deep-learning model to classify the images of cervical lesions. Clinicians could determine patient treatment based on the type of cervix, which greatly improved the diagnostic efficiency and accuracy. The present study was divided into two parts. First, convolutional neural networks were used to segment the lesions in the cervical images; and second, a neural network model similar to CapsNet was used to identify and classify the cervical images. Finally, the training set accuracy of our model was 99%, the test set accuracy was 80.1%, it obtained better results than other classification methods, and it realized rapid classification and prediction of mass image data. 相似文献
3.
In the paper, a convolutional neural network based on quaterniontransformation is proposed to detect median filtering for color images. Compared withconventional convolutional neural network, color images can be processed in a holisticmanner in the proposed scheme, which makes full use of the correlation between RGBchannels. And due to the use of convolutional neural network, it can effectively avoid theone-sidedness of artificial features. Experimental results have shown the scheme’simprovement over the state-of-the-art scheme on the accuracy of color image medianfiltering detection. 相似文献
4.
Devaraj Somasundaram Subramaniam Gnanasaravanan Nirmala Madian 《International journal of imaging systems and technology》2020,30(4):1209-1219
Cervical cancer is identified as the fourth most recurrent cancer among women across the globe. The cancer is treatable, if identified at the early stage. Pap smear test is the most common and the best tool for initial screening of cancer. Pap smear cell level image analysis is an open issue. The limitation of the analysis is due to the complexity of the cell structure. The smear cell image is composed of cytoplasm and nucleus. The shape and structure of the nucleus determines the cancer prevalence. Segmentation of nucleus is an important step in cancer detection. There are various methods developed for nucleus segmentation. The article proposes multithresholding algorithm to segment cytoplasm and nucleus region from the background. Morphological operations are used for correcting the segmented output. Support vector machine classifier is used for classifying the smear cell as normal or abnormal based on the extracted features of the segmented output. The obtained accuracy of the classifier, sensitivity and specificity for single smear cell are 99.66%, 99.85% and 99.17%. 相似文献
5.
The healthcare industry has been significantly impacted by the widespread adoption of advanced technologies such as deep learning (DL) and artificial intelligence (AI). Among various applications, computer-aided diagnosis has become a critical tool to enhance medical practice. In this research, we introduce a hybrid approach that combines a deep neural model, data collection, and classification methods for CT scans. This approach aims to detect and classify the severity of pulmonary disease and the stages of lung cancer. Our proposed lung cancer detector and stage classifier (LCDSC) demonstrate greater performance, achieving higher accuracy, sensitivity, specificity, recall, and precision. We employ an active contour model for lung cancer segmentation and high-resolution net (HRNet) for stage classification. This methodology is validated using the industry-standard benchmark image dataset lung image database consortium and image database resource initiative (LIDC-IDRI). The results show a remarkable accuracy of 98.4% in classifying lung cancer stages. Our approach presents a promising solution for early lung cancer diagnosis, potentially leading to improved patient outcomes. 相似文献
6.
Thendral Natarajan;Lakshmi Devan;Ramaprabha Palayanoor Seethapathy;Senthil Kumar Balakrishnan; 《International journal of imaging systems and technology》2024,34(2):e23036
Cervical cancer is the second most frequent cancer among women of all age groups worldwide. It occurs due to human papillomavirus. In the premature stages, the symptoms will not be predominant until they reach the final stage of cancer. Detection and classification of cervical cancer always demand gynecologists with the necessary skills and experience. The goal of the proposed work is to develop a deep learning framework to facilitate the automated classification of cervical cancer using colposcopy images. The following Deep Convolutional Neural Network (DCNN) models are proposed to detect cervical cancer and classify cervix-type images. (i) the pre-trained DCNNs, namely VGG16, ResNet50, InceptionV3, InceptionResNetV2, and ConvNeXtXLarge (ConvNeXt-XL) using Softmax classifier based on deep features; (ii) the ConvNeXt-XL model with classification using Support Vector Machine (SVM), K Nearest Neighbor (KNN) and Decision Tree (DT) based on deep features; (iii) a customized ConvNeXt-XL network to enhance the classification accuracy using serially concatenated handcrafted and deep features. The research experiment was carried out separately using two datasets: the Cervix-Type dataset (Type 1, Type 2, and Type 3) and the Real Time Cervical dataset (Normal and Abnormal). The simulation outcome confirms that the customized ConvNeXt-XL helped to improve the classification accuracy with the Cervix-Type dataset (>97%) and Real Time Cervical dataset (>98%). 相似文献
7.
Shweta Saxena Sanyam Shukla Manasi Gyanchandani 《International journal of imaging systems and technology》2020,30(3):577-591
Several researchers are trying to develop different computer-aided diagnosis system for breast cancer employing machine learning (ML) methods. The inputs to these ML algorithms are labeled histopathological images which have complex visual patterns. So, it is difficult to identify quality features for cancer diagnosis. The pre-trained Convolutional Neural Networks (CNNs) have recently emerged as an unsupervised feature extractor. However, a limited investigation has been done for breast cancer recognition using histopathology images with CNN as a feature extractor. This work investigates ten different pre-trained CNNs for extracting the features from breast cancer histopathology images. The breast cancer histopathological images are obtained from publicly available BreakHis dataset. The classification models for the different feature sets, which are obtained using different pre-trained CNNs in consideration, are developed using a linear support vector machine. The proposed method outperforms the other state of art methods for cancer detection, which can be observed from the results obtained. 相似文献
8.
验证码是一种区分用户是计算机还是人的公共全自动程序.为了尽可能大批量地获取某网站的信息,就需要让机器可以全自动地识别该网站的验证码.为了破解验证码,对深度学习的验证码图像识别方法进行了研究.提出使用图像标注的方法来生成验证码图像中的字母序列.实验采用深度学习框架Caffe,将卷积神经网络与循环神经网络相结合进行训练.将卷积神经网络的输出用于训练循环神经网络,来不断地预测出序列中下一个最有可能出现的字母.训练的目标是将输出的词尽量和预期的词一致.测试结果表明,该模型能够对该网站的验证码图像做到97%的识别准确率.该方法比只采用卷积神经网络进行识别效果好. 相似文献
9.
Shweta Saxena Sanyam Shukla Manasi Gyanchandani 《International journal of imaging systems and technology》2021,31(1):168-179
Histopathology is considered as the gold standard for diagnosing breast cancer. Traditional machine learning (ML) algorithm provides a promising performance for cancer diagnosis if the training dataset is balanced. Nevertheless, if the training dataset is imbalanced the performance of the ML model is skewed toward the majority class. It may pose a problem for the pathologist because if the benign sample is misclassified as malignant, then a pathologist could make a misjudgment about the diagnosis. A limited investigation has been done in literature for solving the class imbalance problem in computer‐aided diagnosis (CAD) of breast cancer using histopathology. This work proposes a hybrid ML model to solve the class imbalance problem. The proposed model employs pretrained ResNet50 and the kernelized weighted extreme learning machine for CAD of breast cancer using histopathology. The breast cancer histopathological images are obtained from publicly available BreakHis and BisQue datasets. The proposed method achieved a reasonable performance for the classification of the minority as well as the majority class instances. In comparison, the proposed approach outperforms the state‐of‐the‐art ML models implemented in previous studies using the same training‐testing folds of the publicly accessible BreakHis dataset. 相似文献
10.
Calculating the semantic similarity of two sentences is an extremely challenging problem. We propose a solution based on convolutional neural networks (CNN) using semantic and syntactic features of sentences. The similarity score between two sentences is computed as follows. First, given a sentence, two matrices are constructed accordingly, which are called the syntax model input matrix and the semantic model input matrix; one records some syntax features, and the other records some semantic features. By experimenting with different arrangements of representing thesyntactic and semantic features of the sentences in the matrices, we adopt the most effective way of constructing the matrices. Second, these two matrices are given to two neural networks, which are called the sentence model and the semantic model, respectively. The convolution process of the neural networks of the two models is carried out in multiple perspectives. The outputs of the two models are combined as a vector, which is the representation of the sentence. Third, given the representation vectors of two sentences, the similarity score of these representations is computed by a layer in the CNN. Experiment results show that our algorithm (SSCNN) surpasses the performance MPCPP, which noticeably the best recent work of using CNN for sentence similarity computation. Comparing with MPCNN, the convolution computation in SSCNN is considerably simpler. Based on the results of this work, we suggest that by further utilization of semantic and syntactic features, the performance of sentence similarity measurements has considerable potentials to be improved in the future. 相似文献
11.
Skin cancer (melanoma) is one of the most aggressive of the cancers and the prevalence has significantly increased due to increased exposure to ultraviolet radiation. Therefore, timely detection and management of the lesion is a critical consideration in order to improve lifestyle and reduce mortality. To this end, we have designed, implemented and analyzed a hybrid approach entailing convolutional neural networks (CNN) and local binary patterns (LBP). The experiments have been performed on publicly accessible datasets ISIC 2017, 2018 and 2019 (HAM10000) with data augmentation for in-distribution generalization. As a novel contribution, the CNN architecture is enhanced with an intelligible layer, LBP, that extracts the pertinent visual patterns. Classification of Basal Cell Carcinoma, Actinic Keratosis, Melanoma and Squamous Cell Carcinoma has been evaluated on 8035 and 3494 cases for training and testing, respectively. Experimental outcomes with cross-validation depict a plausible performance with an average accuracy of 97.29%, sensitivity of 95.63% and specificity of 97.90%. Hence, the proposed approach can be used in research and clinical settings to provide second opinions, closely approximating experts’ intuition. 相似文献
12.
Ashir Javeed;Peter Anderberg;Muhammad Asim Saleem;Ahmad Nauman Ghazi;Johan Sanmartin Berglund; 《International journal of imaging systems and technology》2024,34(6):e23221
Globally, cancer is the second-leading cause of death after cardiovascular disease. To improve survival rates, risk factors and cancer predictors must be identified early. From the literature, researchers have developed several kinds of machine learning-based diagnostic systems for early cancer prediction. This study presented a diagnostic system that can identify the risk factors linked to the onset of cancer in order to anticipate cancer early. The newly constructed diagnostic system consists of two modules: the first module relies on a statistical F-score method to rank the variables in the dataset, and the second module deploys the random forest (RF) model for classification. Using a genetic algorithm, the hyperparameters of the RF model were optimized for improved accuracy. A dataset including 10 765 samples with 74 variables per sample was gathered from the Swedish National Study on Aging and Care (SNAC). The acquired dataset has a bias issue due to the extreme imbalance between the classes. In order to address this issue and prevent bias in the newly constructed model, we balanced the classes using a random undersampling strategy. The model's components are integrated into a single unit called F-RUS-RF. With a sensitivity of 92.25% and a specificity of 85.14%, the F-RUS-RF model achieved the highest accuracy of 86.15%, utilizing only six highly ranked variables according to the statistical F-score approach. We can lower the incidence of cancer in the aging population by addressing the risk factors for cancer that the F-RUS-RF model found. 相似文献
13.
目的 针对目前印刷套准识别方法依赖于经验人工设计特征提取的问题,提出一种不需要人工提取图像特征的卷积神经网络模型,实现印刷套准状态的识别.方法 采用图像增强技术实现不均衡训练集的均衡化,增加训练集图像的数量,提高模型的识别准确率.设计基于AlexNet网络结构的印刷套准识别模型的结构参数,分析批处理样本数量和基础学习率对模型性能的影响规律.结果 文中方法获得的总印刷套准识别准确率为0.9860,召回率为1.0000,分类准确率几何平均数为0.9869.结论 文中方法能自动提取图像特征,不依赖于人工设计的特征提取方法.在构造的数据集上,文中方法的分类性能优于实验中的支持向量机方法. 相似文献
14.
15.
K. Vijila Rani S. Joseph Jawhar 《International journal of imaging systems and technology》2020,30(4):899-915
Lung tumor is a complex illness caused by irregular lung cell growth. Earlier tumor detection is a key factor in effective treatment planning. When assessing the lung computed tomography, the doctor has many difficulties when determining the precise tumor boundaries. By offering the radiologist a second opinion and helping to improve the sensitivity and accuracy of tumor detection, the use of computer-aided diagnosis could be near as effective. In this research article, the proposed Lung Tumor Detection Algorithm consists of four phases: image acquisition, preprocessing, segmentation, and classification. The Advance Target Map Superpixel-based Region Segmentation Algorithm is proposed for segmentation purposes, and then the tumor region is measured using the nanoimaging theory. Using the concept of boosted deep convolutional neural network yields 97.3% precision, image recognition can be achieved. In the types of literature with the current method, which shows the study's proposed efficacy, the implementation of the proposed approach is found dramatically. 相似文献
16.
The detection of alcoholism is of great importance due to its effects on individuals and society. Automatic alcoholism detection system (AADS) based on electroencephalogram (EEG) signals is effective, but the design of a robust AADS is a challenging problem. AADS’ current designs are based on conventional, hand-engineered methods and restricted performance. Driven by the excellent deep learning (DL) success in many recognition tasks, we implement an AAD system based on EEG signals using DL. A DL model requires huge number of learnable parameters and also needs a large dataset of EEG signals for training which is not easy to obtain for the AAD problem. In order to solve this problem, we propose a multi-channel Pyramidal neural convolutional (MP-CNN) network that requires a less number of learnable parameters. Using the deep CNN model, we build an AAD system to detect from EEG signal segments whether the subject is alcoholic or normal. We validate the robustness and effectiveness of proposed AADS using KDD, a benchmark dataset for alcoholism detection problem. In order to find the brain region that contributes significant role in AAD, we investigated the effects of selected 19 EEG channels (SC-19), those from the whole brain (ALL-61), and 05 brain regions, i.e., TEMP, OCCIP, CENT, FRONT, and PERI. The results show that SC-19 contributes significant role in AAD with the accuracy of 100%. The comparison reveals that the state-of-the-art systems are outperformed by the AADS. The proposed AADS will be useful in medical diagnosis research and health care systems. 相似文献
17.
视频图像中的小像素目标难以检测。针对城市道路视频中的小像素目标,本文提出了一种改进YOLOv3的卷积神经网络Road_Net检测方法。首先,基于改进的YOLOv3,设计了一种新的卷积神经网络Road_Net;其次,针对小像素目标检测更依赖于浅层特征,采用了4个尺度检测方法。最后,结合改进的M-Softer-NMS算法来进一步提高图像中目标的检测精度。为了验证所提出算法的有效性,本文收集并标注了用于城市道路小像素目标物体检测的数据集Road-garbage Dataset,实验结果表明,本文算法能有效地检测出诸如纸屑、石块等在视频中相对于路面的较小像素目标。 相似文献
18.
Recently years, convolutional neural networks (CNNs) have proven to be powerful tools for a broad range of computer vision tasks. However, training a CNN from scratch is difficult because it requires a large amount of labeled training data, which remains a challenge in medical imaging domain. To this end, deep transfer learning (TL) technique is widely used for many medical image tasks. In this paper, we propose a novel multisource transfer learning CNN model for lymph node detection. The mechanism behind it is straightforward. Point-wise (1 × 1) convolution is used to fuse multisource transfer learning knowledge. Concretely, we view the transferred features as priori domain knowledge and 1 × 1 convolutional operation is implemented after pre-trained convolution layers to adaptively combine the transfer information for target task. In order to learn non-linear transferred features and prevent over-fitting, we present an encode process for the pre-trained convolution kernels. At last, based on convolutional factorization technique, we train the proposed CNN model and the encoder process jointly, which improves the feasibility of our approach. The effectiveness of the proposed method is verified on lymph node (LN) dataset: 388 mediastinal LNs labeled by radiologists in 90 patient CT scans, and 595 abdominal LNs in 86 patient CT scans for LN detection. Our method demonstrates sensitivities of about 85%/71% at 3 FP/vol. and 92%/85% at 6 FP/vol. for mediastinum and abdomen respectively, which compares favorably to previous methods. 相似文献
19.
In recent years, deep neural networks have become a fascinating and influential research subject, and they play a critical role in video processing and analytics. Since, video analytics are predominantly hardware centric, exploration of implementing the deep neural networks in the hardware needs its brighter light of research. However, the computational complexity and resource constraints of deep neural networks are increasing exponentially by time. Convolutional neural networks are one of the most popular deep learning architecture especially for image classification and video analytics. But these algorithms need an efficient implement strategy for incorporating more real time computations in terms of handling the videos in the hardware. Field programmable Gate arrays (FPGA) is thought to be more advantageous in implementing the convolutional neural networks when compared to Graphics Processing Unit (GPU) in terms of energy efficient and low computational complexity. But still, an intelligent architecture is required for implementing the CNN in FPGA for processing the videos. This paper introduces a modern high-performance, energy-efficient Bat Pruned Ensembled Convolutional networks (BPEC-CNN) for processing the video in the hardware. The system integrates the Bat Evolutionary Pruned layers for CNN and implements the new shared Distributed Filtering Structures (DFS) for handing the filter layers in CNN with pipelined data-path in FPGA. In addition, the proposed system adopts the hardware-software co-design methodology for an energy efficiency and less computational complexity. The extensive experimentations are carried out using CASIA video datasets with ARTIX-7 FPGA boards (number) and various algorithms centric parameters such as accuracy, sensitivity, specificity and architecture centric parameters such as the power, area and throughput are analyzed. These results are then compared with the existing pruned CNN architectures such as CNN-Prunner in which the proposed architecture has been shown 25% better performance than the existing architectures. 相似文献