Proceedings of The International Conference on Emerging Trends in Artificial Intelligence and Smart Systems, THEETAS 2022, 16-17 April 2022, Jabalpur, India

Research Article

Assessment of Nail Images for Preliminary disease detection and classification based on CNN: The New Horizon in Disease Detection in Humans

Download758 downloads
  • @INPROCEEDINGS{10.4108/eai.16-4-2022.2318168,
        author={Shweta  Marulkar and Bhawna  Narain},
        title={Assessment of Nail Images for Preliminary disease detection and classification based on CNN: The New Horizon in Disease Detection in Humans},
        proceedings={Proceedings of The International Conference on Emerging Trends in Artificial Intelligence and Smart Systems, THEETAS 2022, 16-17 April 2022, Jabalpur, India},
        publisher={EAI},
        proceedings_a={THEETAS},
        year={2022},
        month={6},
        keywords={digital image processing nail features analysis disease prediction convolutional neural network (cnn) inceptionv3 inceptionresnetv2 mobilenetv2 efficientnetb0},
        doi={10.4108/eai.16-4-2022.2318168}
    }
    
  • Shweta Marulkar
    Bhawna Narain
    Year: 2022
    Assessment of Nail Images for Preliminary disease detection and classification based on CNN: The New Horizon in Disease Detection in Humans
    THEETAS
    EAI
    DOI: 10.4108/eai.16-4-2022.2318168
Shweta Marulkar1, Bhawna Narain2,*
  • 1: Research Scholar, MATS School of IT, MATS University, Raipur (C.G.), India
  • 2: Professor, MATS School of IT, MATS University, Raipur (C.G.), India
*Contact email: narainbhawna@gmail.com

Abstract

Digital image processing has wide scope in globe such as military, medical, robotics, forensic science etc. Now a day for such type of applications feature extraction of digital image is important part of processing. Pattern recognition requires the classification of medical imagery. Classification improves the efficiency and accuracy. For the classification process, image preprocessing, fragmentation, and feature extraction might be used. Preliminary disease detection rate depends on all the steps but classification has its own importance in pattern recognition. A Convolutional Neural Network (CNN) model for preliminary disease identification and categorization in humans is presented in this research. The dataset contains 750 images of nails with four symptoms of diseases. We built a CNN model to automate feature extraction and categorization. Color information is frequently employed in the early stages of disease prediction. The filters are applied to three channels based on RGB components in our model. Experiments have said that the proposed approach accurately predicts diseases.