ML &AI -Cascade Convolution Neural Networks and Genetically Optimized Classifiers for Computer-Aided Segmentation of Liver Lesions in CT Scans
AI and ML Based -Age and Gender Prediction Using Deep Convolutional Neural Networks
Gender and age recognition are regarded as critical components of any security, network, or care system. These are commonly used in the case of age-specific content access for children. It is the most efficient method for social media to deliver layered ads while also promoting its reach. When it comes to face recognition, it has indeed advanced to the point where we need to map it in order to achieve the best results.
Deep CNN is used in this paper to improve gender and age prediction from significant outcomes that can be obtained. Significant results can be seen for various tasks such as face recognition. A simple convolutional network architecture is commonly proposed to make a significant improvement in this area.
7. ML &AI -Cascade Convolution Neural Networks and Genetically Optimized Classifiers for Computer-Aided Segmentation of Liver Lesions in CT Scans
Abdominal CT scans are one of the most widely researched and studied subjects by today’s medical professionals. CT scans are extremely effective at detecting abnormal liver function in humans. The primary step that many radiologists use to detect the structure and abnormalities of the liver is computer-aided automatic segmentation.
In this paper, we described deep learning techniques that are most effective for extracting the liver from an abdominal CT scan and then segmenting the lesions from a tumor-ridden liver. Once GA-ANN detects tumours in the liver, a cascade model of convolutional neural networks is the best to use for segmenting lesions.
