A novel solution of deep learning for endoscopic ultrasound image segmentation: enhanced computer aided diagnosis of gastrointestinal stromal tumor

Multimedia Tools and Applications - Gastrointestinal stromal tumor is one of the critical tumors that doctors do not suggest to get frequent endoscopy, so there is a need for a diagnosis system...     

DPV: a taxonomy for utilizing deep learning as a prediction technique for various types of cancers detection

Multimedia Tools and Applications - Deep learning (DL) is a type of machine learning capable of processing large quantities of data to provide analytic results based on a particular...     

A novel secure solution of using mixed reality in data transmission for bowel and jaw surgical telepresence: enhanced rivest cipher RC6 block cipher

Security threats are crucial challenges that deter Mixed reality (MR) communication in medical telepresence. This research aims to improve the security by reducing the chances of types of various attacks occurring during the real-time data transmission in surgical telepresence as well as reduce the time of the cryptographic algorithm and keep the quality of the media used. The proposed model consists of an enhanced RC6 algorithm in combination. Dynamic keys are generated from the RC6 algorithm mixed with RC4 to create dynamic S-box and permutation table, preventing various known attacks during the real-time data transmission. For every next session, a new key is created, avoiding possible reuse of the same key from the attacker. The results obtained from our proposed system are showing better performance compared to the state of art. The resistance to the tested attacks is measured throughout the entropy, Pick to Signal Noise Ratio (PSNR) is decreased for the encrypted image than the state of art, structural similarity index (SSIM) closer to zero. The execution time of the algorithm is decreased for an average of 20%. The proposed system is focusing on preventing the brute force attack occurred during the surgical telepresence data transmission. The paper proposes a framework that enhances the security related to data transmission during surgeries with acceptable performance.

     

A novel augmented reality visualization in jaw surgery: enhanced ICP based modified rotation invariant and modified correntropy

Image registration, accuracy, processing time and occlusions are the main limitations of augmented reality (AR) based jaw surgery. Therefore, the main aim of this paper is to reduce the registration error, which will help in improving the accuracy and reducing the processing time. Also, it aims to remove outliers and remove the registration outcomes trapped in local minima to improve the alignment problems and remove the occlusion caused by surgery instrument. The enhanced Iterative Closest Point (ICP) algorithm with rotation invariant and correntropy was used for the proposed system. Markerless image registration technique was used for AR-based jaw surgery. The problem of occlusion caused by surgical tools and blood is solved by using stereo based tracing with occlusion handling techniques. This research reduced alignment error 0.59 mm?~?0.62 mm against 0.69?~?0.72 mm of state-of-the-art solution. The processing time of video frames was enhanced to 11.9?~?12.8 fps against 8?~?9.15 fps in state-of-the-art solution. This paper is focused on providing fast and accurate AR-based system for jaw surgery. The proposed system helps in improving the AR visualization during jaw surgery. The combination of methods and technology helped in improving AR visualization for jaw surgery and to overcome the failure caused by a large rotation angle and provides an initial parameter for better image registration. It also enhances performance by removing outliers and noises. The pose refinement stage provides a better result in terms of processing time and accuracy.

     

A novel deep learning neural network for fast-food image classification and prediction using modified loss function

Multimedia Tools and Applications - Accurate food image classification is often critical to accurately monitor the dietary assessment to reduce the risk of different heart-related diseases,...     

Enhancing the prediction of type 2 diabetes mellitus using sparse balanced SVM

Multimedia Tools and Applications - The natural population-based prediction of type 2 diabetes is costly since it needs a high number of resources. Even though much research has used machine...     

Deep learning neural network for texture feature extraction in oral cancer: enhanced loss function

Multimedia Tools and Applications - The use of a binary classifier like the sigmoid function and loss functions reduces the accuracy of deep learning algorithms. This research aims to increase the...     

A novel modified undersampling (MUS) technique for software defect prediction

Background and aim: Many sophisticated data mining and machine learning algorithms have been used for software defect prediction (SDP) to enhance the quality of software. However, real‐world SDP data sets suffer from class imbalance, which leads to a biased classifier and reduces the performance of existing classification algorithms resulting in an inaccurate classification and prediction. This work aims to improve the class imbalance nature of data sets to increase the accuracy of defect prediction and decrease the processing time . Methodology: The proposed model focuses on balancing the class of data sets to increase the accuracy of prediction and decrease processing time. It consists of a modified undersampling method and a correlation feature selection (CFS) method. Results: The results from ten open source project data sets showed that the proposed model improves the accuracy in terms of F1‐score to 0.52 ～ 0.96, and hence it is proximity reached best F1‐score value in 0.96 near to 1 then it is given a perfect performance in the prediction process. Conclusion: The proposed model focuses on balancing the class of data sets to increase the accuracy of prediction and decrease processing time using the proposed model.     

Deep learning for vision‐based fall detection system: Enhanced optical dynamic flow

Accurate fall detection for the assistance of older people is crucial to reduce incidents of deaths or injuries due to falls. Meanwhile, vision‐based fall detection system has shown some significant results to detect falls. Still, numerous challenges need to be resolved. The impact of deep learning has changed the landscape of the vision‐based system, such as action recognition. The deep learning technique has not been successfully implemented in vision‐based fall detection system due to the requirement of a large amount of computation power and requirement of a large amount of sample training data. This research aims to propose a vision‐based fall detection system that improves the accuracy of fall detection in some complex environments such as the change of light condition in the room. Also, this research aims to increase the performance of the pre‐processing of video images. The proposed system consists of Enhanced Dynamic Optical Flow technique that encodes the temporal data of optical flow videos by the method of rank pooling, which thereby improves the processing time of fall detection and improves the classification accuracy in dynamic lighting condition. The experimental results showed that the classification accuracy of the fall detection improved by around 3% and the processing time by 40–50 ms. The proposed system concentrates on decreasing the processing time of fall detection and improving the classification accuracy. Meanwhile, it provides a mechanism for summarizing a video into a single image by using dynamic optical flow technique, which helps to increase the performance of image preprocessing steps.     

A novel enhanced region proposal network and modified loss function: threat object detection in secure screening using deep learning

The Journal of Supercomputing - Detection of threat objects concealed in passenger clothing and baggage poses a challenge to aviation security. At present, the detection technology is capable of...