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...     

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 solution of deep learning for sleep apnea detection: enhancement of SC and elimination of GVICS

Multimedia Tools and Applications - Convolutional neural network (CNN) classification has not achieved a medically-satisfied level of accuracy in sleep apnea detection due to the negative effect of...     

A novel augmented reality for hidden organs visualisation in surgery: enhanced super-pixel with sub sampling and variance adaptive algorithm

In recent years, Augmented Reality (AR) has gained more attention as an effective tool in medical surgeries. The potentials of using AR in the medical field can change conventional medical procedures. However, the technology still facing fundamental challenges, especially hidden organs, for example, the organs behind the bowel and liver. The surgeries in these areas lack accuracy in the visualization of the soft tissues behind the bowel and liver like the uterus and gall bladder. This research aims to improve the accuracy of visualisation and the processing time of the augmented video. The proposed system consists of an enhanced super-pixel algorithm with variance weight adaptation and subsampling method. The simulation studies show significant improvements in visualization accuracy and a reduction in processing time. The results show reduced visualisation error by 0.23 mm. It provides better accuracy of the video in terms of visualization error from 1.58?~?1.83 mm to 1.35?~?1.60 mm, and the processing time decreases from 50?~?58 ms/frames to 40?~?48 ms/frames. The proposed system \ focused on the pixel refinement for the 3d reconstruction of the soft tissue, which helps solve the issue of visualising the bowel and liver in an augmented video.

     

Enhanced classification loss functions and regularization loss function (ECLFaRLF) algorithm for bowel cancer feature classification

Multimedia Tools and Applications - Bowel cancer is one of the most common cancers as stated in the bowel cancer cases statistics. The proposed technique is to recognize the pattern of tissue...