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

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

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.

Pothole Detection Using Transfer Learning Models: A Comparative Study