Convolutional Neural Networks for Medical Image Diagnosis and Prognosis

One of the most incredible machine learning methods is deep learning. Utilised for picture categorization, clinical archiving, item identification, and other purposes. The quantity of medical image archives is expanding at an alarming rate as hospitals employ digital photos for documentation more frequently. Digital imaging is essential for assessing the severity of a patient's illness. Medical imaging has a wide variety of uses in research and diagnostics. Due to recent developments in image processing technology, self-operating identification of medical photos is still a research area for computer vision researchers. We require an appropriate classifier in order to categorise medical photos using various classifiers. After organ prediction and classification, the research was modified to include medical picture recognition. For medical picture detection, pretrained convolutional networks and Kmean clustering techniques similar to those used for organ identification are employed. Separating the training from the test data allowed for the data's authentication. The application of this strategy has been proven to be most effective for categorising various medical images of human organs

Exploiting Interslice Correlation for MRI Prostate Image Segmentation, from Recursive Neural Networks Aspect

Segmentation of the prostate from Magnetic Resonance Imaging (MRI) plays an important role in prostate cancer diagnosis. However, the lack of clear boundary and significant variation of prostate shapes and appearances make the automatic segmentation very challenging. In the past several years, approaches based on deep learning technology have made significant progress on prostate segmentation. However, those approaches mainly paid attention to features and contexts within each single slice of a 3D volume. As a result, this kind of approaches faces many difficulties when segmenting the base and apex of the prostate due to the limited slice boundary information. To tackle this problem, in this paper, we propose a deep neural network with bidirectional convolutional recurrent layers for MRI prostate image segmentation. In addition to utilizing the intraslice contexts and features, the proposed model also treats prostate slices as a data sequence and utilizes the interslice contexts to assist segmentation. The experimental results show that the proposed approach achieved significant segmentation improvement compared to other reported methods