A Python-based computer vision and AI system for skin disease recognition and diagnosis. Led end-to-end project pipeline, including data gathering, preprocessing, and training models. Utilized Keras, TensorFlow, OpenCV, and other libraries for image processing and CNN models, showcasing expertise in deep learning and machine learning techniques.

There has been numeruous advancements towards utilizing deep networks, ANNs, AI, etc in tasks like detecting the skin disease, type of tumour, etc. However, it becomes difficult for the networks to learn the features since, most of the skin images are occluded by hair. Thus, there is a need for pre-processing of the skin images to remove these obstructing hair. This sample project aims to remove the hair noise from the skin image with the help of Morphological filtering.

Skin Disease Detection and Treatment Susceptibility by AI

AI-based localization and classification of skin disease with erythema

Dermatrix is an AI-powered tool designed to help identify and provide information about common skin conditions. The application uses deep learning technology to analyze images of skin and compare them against a database of known skin diseases.

Dermatological Issues/disorders are most commonly spread worldwide. This can be caused by various fungal, bacterial, or skin allergies. Effective use of Emerging technologies like AI/ML can recognize such diseases. Computer Vision is one such platform that made the possibility of detecting the cause accurately through Images.The problem here is to develop an Application Programming Interface which can be easily integrated with Android app to detect the skin disease without any physical interaction with a Dermatologist.

🎯 DermaScan – AI-Powered Skin Disease Detection Web App A full-stack web application that detects skin diseases using deep learning and provides personalized insights based on image uploads or live camera input.

AI-powered skin disease classifier using an ensemble of EfficientNetV2, ConvNeXt, and Vision Transformer models across 22 conditions. Features GradCAM++ heatmaps, uncertainty quantification, high-risk alerts, PDF reports, and a dark clinical web UI. Built with TensorFlow, Flask, and OpenCV. For research use only.

🎯 State-of-the-art AI model for skin disease classification with advanced class balancing techniques and mobile deployment optimization.

Final Year College Project made on Machine Learning algorithm with Project Report, Project Code, Research Paper, PPT, Synopsis and Video Explanation

A dermatology backend with skin disease image AI-model fine-tuned using Yolov8 pre-trained model. The app aims to educate people about skin diseases and help them to recognise skin diseases on themselves.

An AI based application to detect skin disease with remote doctor assistance.

[NeurIPS'25] Doctor Approved: Generating Medically Accurate Skin Disease Images through AI-Expert Feedback

Skin cancer is the most prevalent type of cancer. Melanoma, specifically, is responsible for 75% of skin cancer deaths, despite being the least common skin cancer. The American Cancer Society estimates over 100,000 new melanoma cases will be diagnosed in 2020. It's also expected that almost 7,000 people will die from the disease. As with other cancers, early and accurate detection—potentially aided by data science—can make treatment more effective. Currently, dermatologists evaluate every one of a patient's moles to identify outlier lesions or “ugly ducklings” that are most likely to be melanoma. Existing AI approaches have not adequately considered this clinical frame of reference. Dermatologists could enhance their diagnostic accuracy if detection algorithms take into account “contextual” images within the same patient to determine which images represent a melanoma. If successful, classifiers would be more accurate and could better support dermatological clinic work. As the leading healthcare organization for informatics in medical imaging, the Society for Imaging Informatics in Medicine (SIIM)'s mission is to advance medical imaging informatics through education, research, and innovation in a multi-disciplinary community. SIIM is joined by the International Skin Imaging Collaboration (ISIC), an international effort to improve melanoma diagnosis. The ISIC Archive contains the largest publicly available collection of quality-controlled dermoscopic images of skin lesions. In this competition, you’ll identify melanoma in images of skin lesions. In particular, you’ll use images within the same patient and determine which are likely to represent a melanoma. Using patient-level contextual information may help the development of image analysis tools, which could better support clinical dermatologists. Melanoma is a deadly disease, but if caught early, most melanomas can be cured with minor surgery. Image analysis tools that automate the diagnosis of melanoma will improve dermatologists' diagnostic accuracy. Better detection of melanoma has the opportunity to positively impact millions of people.

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Our code files for the paper: Enhancing Skin Disease Classification Leveraging Transformer-based Deep Learning Architectures and Explainable AI

AI-powered skin disease classification system using a fine-tuned ResNet CNN with Grad-CAM explainability. Built with PyTorch and Streamlit to predict 8 skin conditions and visualize model attention for transparent, confidence-aware predictions.

DermSafe is an AI Based Android application intended to provide assistance to people with various Skin Diseases in identifying and treating their ailments. This is a team project consisting of Kushagra Srivastava, Nikhil Jain, Nhan Ton, and Rebecca Wang, and was created for HackUMass VIII (18-20 December, 2020).

Skin Cancer & Diseases Classification CNN Model for Intel Gen AI Hackathon - Cognizance

DermAI is a Flutter-based Android application designed to diagnose five of the most prevalent skin diseases using advanced machine learning models. The application leverages deep learning to analyze images of skin conditions and provides users with a probable diagnosis.

DermAssist is an AI-powered platform for skin disease detection and insights. It uses deep learning (CNN & Transfer Learning) with OpenCV for image preprocessing to classify conditions and NLP-based explanations to deliver clear, user-friendly disease info. Built with Python, TensorFlow/Keras, and deployed via Flask/Streamlit.

// AI agent and Deep Learning powered Skin Cancer and Disease prediction

This is react native application which uses a machine learning model to predict skin diseases and to give information about the predicted disease vertex AI was use.

🩺🤖 AI-powered skin disease classifier using EfficientNetB0. Detects 22 dermatological conditions with Flask web interface. Built with TensorFlow & Keras. 🧬✨

MediLink: Your all-in-one healthcare assistant powered by AI. Predicts skin diseases, analyzes medical reports, simulates heart rates, offers prompt-based diagnosis, and enables telemedicine. Transforming healthcare with advanced technology.

This is an Agentic AI application that uses a CNN model for skin disease classification and provides healthcare recommendations using LangChain,Azure AI search and Azure OpenAI (GPT-4o) model. Built with Django REST Framework, ReactJs Library, and PostgreSQL for seamless interaction and data management.

Built an AI skin disease classification with VGG19, hitting 86.8% accuracy across 9 conditions with real-time confidence scores. Developed a full-stack React (TypeScript) + Flask app for secure, interactive medical analysis. Delivered a privacy-first solution, processing images locally and showcasing TensorFlow, Python, and modern web skills.

It aims to provide a comprehensive solution to skin diseases by combining image processing and the RAG method.

AI- Based Skin Disease Detection

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