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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.
The goal of this project is to develop a tumor detection model utilizing logistic regression. This model aims to analyze medical imaging data and predict the likelihood of the presence of tumors.
scMalignantFinder is a Python package specially designed for analyzing cancer single-cell RNA-seq datasets to distinguish malignant cells from their normal counterparts.
The Brain Tumor MRI Dataset from Kaggle is employed for automated brain tumor detection and classification research. Investigated methods include using pre-trained models (VGG16, ResNet50, and ViT). 🧠🔍
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.
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.