Classify Skin cancer from the skin lesion images using Image classification. The dataset for the project is obtained from the Kaggle SIIM-ISIC-Melanoma-Classification competition.

🚞 Skin 🚒 Cancer 🚂 Classification 🚋 is a computer ✈ vision and 🚀deep learning 🛼 that uses 🛸 Convolutional ⛱ Neural 🌈 Networks to 🚤 classify 🕌 skin lesions 🏘 and detect 🏟 potential 🏜 skin cancer 🏛 from 🏬 dermatology 🏥 images The 🚄 project aims 🏯 to support ⚽ early diagnosis ⚾ and treatment 🥎 through AI 🏐 powered image 🥊 analysis

Deep Learning-based Skin Cancer Classification using FastAI and ResNet | Includes dataset, Jupyter Notebook, PR & ROC curves, confusion matrix, and literature review.

As skin cancer is one of the most frequent cancers globally, accurate, non-invasive dermoscopy-based diagnosis becomes essential and promising. A task of our Deep Learning CNN model is to predict seven disease classes with skin lesion images.

Skin cancer classification project using deep learning techniques for automated diagnosis of skin lesions.

This project is part of the ISIC Challenge for skin cancer classification. It uses deep learning with EfficientNetB3 to classify images as cancerous or non-cancerous. The model is trained using TensorFlow and Keras, with data augmentation and fine-tuning techniques to improve performance.

A Multimodal Deep Learning Approach for Skin Cancer Classification using ViTs (Visual Transformers)

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Skin cancer classification using deep learning

My Mtech Final year research project.

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AI-powered skin cancer detection using deep learning and image classification.

Deep learning–based skin cancer (melanoma) classification system using dermoscopic images, with DullRazor hair artifact removal, CLAHE contrast enhancement, a fine‑tuned DenseNet121 model, and Grad‑CAM explainability for clinically interpretable predictions.

This repository contains a deep learning model for skin cancer classification using the InceptionV3 architecture. The model was trained on the HAM10000 dataset and is designed with computational efficiency in mind. It was developed to be able to run on a CPU.

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Skin Cancer classification using deep learning

Group12

Skin-Cancer-Classification is a Deep Learning project for skin cancer classification using CNNs.

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Ablation study comparing different deep learning architectures for skin cancer classification using the HAM10000 dataset.

Skin cancer detection and classification using deep learning with Grad-CAM visualization and Streamlit web app.

AI-powered Streamlit web application for detecting 5 common skin diseases (Eczema, Psoriasis, Vitiligo, Tinea, Skin Cancer) plus normal skin classification using Deep Learning.

A CNN-based skin cancer classification system built in Python to detect 9 types of skin cancer from ISIC dermoscopic images using deep learning and data augmentation.

Binary and multi-class classification of skin cancer from dermoscopic images using advanced image processing, machine learning and deep learning approach.

Skin Cancer Classification using HAM10000 Dataset – a deep learning project employing Convolutional Neural Networks to classify various types of pigmented skin lesions from dermatoscopic images, aiming to assist with early skin cancer detection.

Developed a deep learning model using EfficientNetB0 to detect skin cancer from medical images. Handled class imbalance with data augmentation and achieved high classification accuracy.

Repository for Melanoma Classification Model Using Deep Learning. Developed for accurate early diagnosis of skin cancer. Contributors: Mohammed Al-Kulaib, Khaled Al-Qahtani, Saad Al-Dossari, Fahad Al-Taher. Supervisor: Dr. Mohammed Abu Al-Rub

This repository implements deep learning–based techniques for skin cancer image classification using dermoscopic images. It includes CNN models such as ResNet, EfficientNet, and a hybrid ResNet–EfficientNet architecture, with preprocessing, augmentation, and performance evaluation using standard metrics.

A web-based application for skin cancer detection using deep learning (ResNet50), explainable AI (Grad-CAM), and a fuzzy logic system for medical recommendations. Users can upload skin lesion images, receive classification results with confidence scores, view model explanations, and get intelligent medical advice.

This repository contains Jupyter notebooks implementing deep learning models for Skin Cancer Detection and Segmentation. The project progresses from simple binary/multi-class classification to advanced segmentation using U-Net variants and hybrid models. It uses datasets like HAM10000 (for 2/7 labels) and DermNet/Skin Disease (for 9 classes).