Skin Disease Classification Using Image Analysis

EasyChair Preprint 10128

Abstract

Skin diseases affect millions of people worldwide and can have a significant impact on their quality of life. Early and accurate diagnosis is crucial for effective treatment and management of these diseases. In this paper, we propose a machine learning model for predicting skin diseases. The research involved collecting a dataset of dermatological images, preprocessing the images, extracting relevant features using various image analysis techniques, and training and evaluating machine learning models for disease classification. Our model utilizes a deep convolutional neural network architecture that is trained on the dataset to learn and extract features from the images. We also incorporate a heatmap visualization technique to highlight the regions of the images that the model relies on for its predictions. To evaluate our model's performance, we calculate accuracy metrics and generate accuracy graphs that show the model's performance on different skin diseases. Our results demonstrate that our model achieved high accuracy in predicting various skin diseases, and our visualization techniques provide additional insights into the model's decision-making process. Our proposed approach has the potential to improve the diagnosis and treatment of skin diseases, leading to better outcomes for patients.

Keyphrases: AI, CNN, Disease Identification, Python, neural networks

@booklet{EasyChair:10128,
  author    = {Paramjeet Singh and Ardhendhu Neogi and Nipun Sharma and Tarun Maini},
  title     = {Skin Disease Classification Using Image Analysis},
  howpublished = {EasyChair Preprint 10128},
  year      = {EasyChair, 2023}}