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R package that automatically classifies the cells in the scRNA data by segregating non-malignant cells of tumor microenviroment from the malignant cells. It also infers the copy number profile of malignant cells, identifies subclonal structures and analyses the specific and shared alterations of each subpopulation.
In this we trained a model to detect if there is a tumor in the brain image given to the model. Meaning a model for binary class with an accuracy of above 90 for same and cross validation.
scMalignantFinder is a Python package specially designed for analyzing cancer single-cell RNA-seq datasets to distinguish malignant cells from their normal counterparts.
A Real-Time Multi Class Brain Tumor Classifier to Classify Brain Tumors using ConvNets (CNNs) and ANN as an asset of Deep Learning and to examine the position of the tumor.
As part of the Thesis project, an Artificial Intelligence model was trained to classify breast tumors from ultrasound images as benign or malignant. Additionally, a user-friendly web application was created with Python for the backend and ReactJS for the frontend.