This project utilizes InceptionResNetV2 for brain tumor classification. Trained on a curated dataset, the model distinguishes between tumor and non-tumor brain images. With GPU acceleration, it ensures efficient training, and results are presented through metrics, graphs, and random image predictions. A valuable tool for medical image analysis. 🌐�

Fine-Tune Pretrained Image Classification Models in MATLAB

Pothole Detection Using Transfer Learning Models: A Comparative Study

Based on our paper "A fuzzy rank-based deep ensemble methodology for multi-class skin cancer classification" published in Scientific Reports (Nature)

This repository implements a deep learning pipeline for skin lesion classification using the InceptionResNetV2 architecture on the HAM10000 dermoscopic image dataset, including data preprocessing, augmentation, class imbalance handling, and detailed performance evaluation.