Skin Disease Detection Train Image Data Flask in Python Projects

Skin Disease Detection Train Image Data Flask in Python Projects
Abstract
Skin diseases are among the most common health issues globally and require timely diagnosis to prevent complications and ensure effective treatment. Due to the shortage of dermatologists in many regions, automated diagnosis systems have gained significant importance. This project presents an intelligent skin disease detection system that uses a deep learning CNN model trained on dermatological image data to classify different types of skin diseases. The system processes skin lesion images and predicts the disease category with high accuracy. The trained model is deployed using a Flask web framework in Python, enabling users to upload images through a simple web interface for real-time predictions. The project integrates computer vision, medical image analysis, and web deployment to deliver a fast, affordable, and accessible diagnostic aid that supports early disease detection.

Existing System
The existing system of skin disease diagnosis primarily depends on physical examination by dermatologists or biopsy-based laboratory procedures. These manual methods are time-consuming, expensive, and limited by the availability of medical experts. In rural or resource-limited areas, patients often face delays in diagnosis which can worsen medical conditions. Current semi-automated systems only assist dermatologists by enhancing images or providing basic visual metrics but lack automated disease classification. Several mobile skin checker applications exist but most of them rely on shallow machine learning techniques with low accuracy and fail to differentiate between similar disease patterns due to limited feature extraction capabilities. Additionally, many of these platforms are not open-source and do not offer explainable diagnosis or real-time web deployment.

Proposed System

The proposed system uses deep learning-based image classification for detecting skin diseases from dermoscopic images. A Convolutional Neural Network model is trained on labeled skin lesion datasets such as HAM10000 or ISIC to identify various diseases like melanoma, ringworm, eczema, psoriasis, and benign keratosis. The images undergo preprocessing including resizing, normalization, noise removal, data augmentation, and segmentation to improve feature extraction and classification accuracy. The model is trained and tested using TensorFlow or Keras and evaluated based on metrics such as accuracy, precision, recall, and F1 score. The trained CNN model is deployed within a Flask web application where users can upload a skin image and instantly receive a disease prediction along with confidence scores. The system offers real-time usability, scalability, user-friendly interface and can be extended by integrating medical recommendations or telemedicine support for professional consultation.

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