Transforming Dermatology: AI-Powered Skin Disease Diagnosis Prototype

Table of Contents

1. Introduction
2. Problem Statement
3. Solution: AI-Based Rule for Preliminary Diagnosis of Dermatological Diseases
   1. Web Application for Image Upload and Prediction
   2. Skin Assistant Chatbot
4. Technology Used
   1. Training Data Set
   2. Machine Learning Model
   3. Accuracy of the Model
5. Future Plans
   1. Expansion to More Diseases and Better Data
   2. Hyper Tuning and Improved Accuracy
   3. Connecting Users to Dermatologists
   4. Use of Large Language Model-based Chatbot
6. Conclusion

AI-Based Rule for Preliminary Diagnosis of Dermatological Diseases

In today's technologically advanced world, artificial intelligence (AI) has permeated various industries, revolutionizing traditional practices and improving efficiency. In the field of dermatology, AI has the potential to aid in the early diagnosis of various skin conditions, leading to Timely intervention and improved patient outcomes. This article explores a web application with an AI-based rule that allows users to upload images of their skin conditions and receive a preliminary diagnosis. Additionally, a chatbot provides users with information about dermatological conditions, causes, and prevention methods.

Introduction

The field of dermatology deals with the diagnosis and treatment of diseases related to the skin, hair, and nails. Timely and accurate diagnosis is crucial for effective treatment and management of such conditions. However, dermatologists often face challenges in diagnosing skin diseases due to the wide variety of symptoms and conditions. This is where AI can play a significant role by leveraging machine learning algorithms to analyze skin images and provide preliminary diagnoses.

Problem Statement

The problem statement for this project was to develop an AI-based rule for the preliminary diagnosis of dermatological diseases. The aim was to Create a solution that could assist dermatologists and individuals in identifying potential skin conditions by analyzing uploaded images. The solution aimed to be accurate, efficient, and accessible to users.

Solution: AI-based Rule for Preliminary Diagnosis of Dermatological Diseases

Web Application for Image Upload and Prediction

To address the problem statement, a web application was developed. Users can visit the application's home page and upload images of their skin conditions. Upon image upload, the application utilizes an AI-based algorithm to analyze the image and predict if the user has a specific skin condition. The prediction results are displayed to the user, along with the accuracy of the diagnosis.

Skin Assistant Chatbot

In addition to the image upload and prediction functionality, the web application also features a chatbot called the "Skin Assistant." The chatbot serves as a user-friendly interface for interacting with the application and obtaining information about dermatological conditions. Users can ask the chatbot questions about specific conditions, causes, prevention methods, and more. The chatbot provides Relevant answers utilizing an intent-based approach and leveraging the Wikipedia API for information retrieval.

Technology Used

Training Data Set

The development of an accurate and reliable AI-based rule requires a robust training data set. In this project, a data set comprising of 7000+ images was used for training and validation purposes. The data set encompassed various dermatological conditions, including acne, vitiligo, fungal infections, melanoma skin cancer, and eczema. Additionally, a sixth class was created for normal skin images. The diversity and size of the data set contributed to the effectiveness of the machine learning model.

Machine Learning Model

To train the AI model, transfer learning was employed. The model selected for this project was the EfficientNet-B3, which had already been pre-trained on a large collection of images. Transfer learning allowed the model to leverage the existing knowledge gained from the pre-training phase and Apply it to the specific task of dermatological disease diagnosis. The use of a pre-trained model significantly improved the accuracy and efficiency of the prediction process.

Accuracy of the Model

The accuracy of the machine learning model was assessed during the training and testing phases. In the training phase, the model achieved an accuracy of 97%, indicating its ability to learn and generalize from the provided data set. During testing, the model achieved an accuracy of 88%, demonstrating its effectiveness in diagnosing dermatological conditions based on uploaded images. These accuracy scores indicate the reliability and potential of the AI-based rule for preliminary diagnosis.

Future Plans

The development of the web application and AI-based rule for preliminary diagnosis of dermatological diseases offers immense potential for further growth and improvement. The following future plans have been identified to enhance the solution:

Expansion to More Diseases and Better Data

To improve the effectiveness and coverage of the AI-based rule, it is essential to expand the range of diseases that can be diagnosed. Acquiring a more diverse and extensive data set would contribute to training the model on rarer and more complex conditions, thereby enhancing its diagnostic accuracy.

Hyper Tuning and Improved Accuracy

While the Current model achieves commendable accuracy, fine-tuning and hyperparameter optimization can further improve the accuracy of the preliminary diagnosis. Continual efforts in refining the model's performance and reducing false positives or false negatives will enhance its reliability and trustworthiness.

Connecting Users to Dermatologists

To provide additional value to users, the future plan includes integrating a feature that connects users to trusted dermatologists located near them. This connection would enable users to Seek further guidance, recommendations, or professional medical advice based on their uploaded images and preliminary diagnoses. This feature could serve as an additional source of revenue for the application.

Use of Large Language Model-based Chatbot

To augment the capabilities of the existing Skin Assistant chatbot, the plan is to implement a large language model-based chatbot. This advanced chatbot would be trained on a vast array of dermatological knowledge, allowing it to cover a broader range of queries and provide more detailed responses. The use of a large language model would enhance user experience and ensure accurate and informative interactions.

Conclusion

The AI-based rule for the preliminary diagnosis of dermatological diseases offers a promising solution to the challenges faced in the field of dermatology. The web application's image upload and prediction functionality, coupled with the Skin Assistant chatbot, provide users with a convenient and accessible means of obtaining preliminary diagnoses and information about skin conditions. With future plans focusing on expansion, accuracy improvement, and additional features, the solution has the potential to revolutionize how dermatological diseases are diagnosed and addressed.