The project for NTU's course on Machine Learning, CZ4041

Using an External dataset to get the pre-trained weights of the NIH dataset and training on the provided dataset to detect the presence of pneumonia.

Improved Deep Learning Model has been used to classify Breast Cancer from Histopathological Tissue Images.

Comparison between different DL models such as VGGnet,InceptionV3,Resnet for copy move forgery detection

Implementation of some basic Image Annotation methods (using various loss functions & threshold optimization) on Corel-5k dataset with PyTorch library

Development and analysis of various deep NN models to detect glaucoma cases from fundus images. The performance of the best model was evaluated with cross-validation. Mean F1-score: 0.95975, with a standard deviation of 0.02274.

IPython Notebook to build the model for Dog Breed Prediction

Flower image classification using Transfer learning (Xception)

The project focuses on classifying brain tumors using the Multi-Modal Squeeze and Excitation Network.

In this project, we used a transfer learning approach to build an image classification model for the classification of skin lesion, we trained our model specifically on the ham10000 dataset available on kaggle and we were able to achieve a 93.6% accuracy

This repository hosts the Cervical Cancer Image Classification project, a comprehensive effort aimed at improving the classification accuracy of Squamous Cell Carcinoma (SCC) through advanced deep learning models and ensemble techniques. The project utilizes the Herlev dataset.