Hair Disease Classification System Using Deep Learning

Overview

The Hair Disease Classification System is a deep learning-based application built to identify various hair and scalp conditions from medical images. Utilizing the power of the InceptionV3 architecture, the system offers a fast and reliable method for preliminary diagnosis. Designed for dermatology professionals, students, and telehealth providers, the tool simplifies disease recognition through an intuitive web interface—making complex image classification accessible and informative without requiring specialized hardware or backend systems.

Client Objective

The client aimed to create a simple yet effective AI-driven tool to classify hair diseases from scalp images. Their primary goal was to support early diagnosis and medical education by offering a lightweight, web-based solution that could deliver accurate predictions without relying on heavy infrastructure. The tool needed to be user-friendly, fast, and capable of handling multiple disease categories through image uploads.

Our Approach

We adopted a transfer learning strategy using the InceptionV3 model pretrained on ImageNet and fine-tuned it on a curated dataset of hair disease images. The images were preprocessed for consistency and resized to 299×299 pixels. We developed a lightweight web application using Streamlit, allowing users to upload scalp images and instantly receive classification results with confidence scores. The model was optimized for performance and integrated with a clean, intuitive frontend to ensure ease of use for both medical professionals and learners.

Key Features

• Pretrained Deep Learning Model
  The system leverages a fine-tuned InceptionV3 model, pretrained on ImageNet, to accurately classify multiple hair disease categories with minimal training time and high reliability.
• Real-Time Predictions
  Users can upload images and receive immediate classification results, along with confidence scores, enabling quick assessments without delays or external dependencies.
• User-Friendly Interface
  A clean and minimal web interface built using Streamlit allows seamless interaction, making the application accessible even to non-technical users.
• Multi-Class Classification Support
  The model is trained to identify a range of common hair and scalp disorders, helping users distinguish between conditions like dandruff, alopecia, and fungal infections.
• Lightweight & Deployable Anywhere
  With no reliance on GPU or heavy backend systems, the application runs smoothly on standard devices, making it ideal for demonstrations, learning, and remote usage.
• Robust Image Preprocessing
  Automated image resizing and normalization ensure consistent input quality, which improves model accuracy and reliability across diverse image sources.
• Confidence Score Display
  The app provides probability percentages for each predicted class, offering transparency and helping users understand the certainty of each diagnosis.
• Open-Source Dataset Training
  The model was trained on a carefully curated Kaggle dataset, ensuring a diverse range of hair disease images for comprehensive learning.
• Educational Resource
  Beyond diagnostics, the tool serves as a learning aid for medical students and practitioners by illustrating key visual differences between hair conditions.

Impact

The Hair Disease Classification System significantly enhanced the ability to identify hair and scalp diseases quickly and accurately. By automating the classification process, it reduced the need for extensive manual examination, saving valuable time for practitioners and increasing accessibility for users in remote or resource-limited areas. This system offers a promising tool for early detection and improved patient care through AI-driven insights.