A deep learning model built using TensorFlow to classify breast cancer tumors as malignant or benign using the Breast Cancer dataset.

[MedIA2023 & MICCAI2022 ] Ambiguity-aware breast tumor cellularity estimation via self-ensemble label distribution learning

Cellular​ ​signatures​ characterisation ​after​ ​carcinogenic​ ​chemical​ ​exposure​ ​in​ ​mammalian​ ​tissue.

Breast Cancer Prediction using Machine Learning This project implements a supervised learning model for breast cancer classification, helping in early detection and improving medical analysis.

Breast Cancer Prediction with Logistic Regression Classification gives an accuracy of 96.70%. apart from this Decision Tree Classification gives more accuracy along with LRC. Dataset can be available on UCI Machine Learning.

image classification on CIFAR-10 with ResNet, medical image analysis on breast histopathology images using CNNs, and image captioning on Flickr8k, Flickr30k, and MSCOCO datasets with advanced architectures like LSTM and attention mechanisms.

Patient subgrouping with distinct survival rates via integration of multiomics data on a Grassmann manifold