Classification of Medical Images Through Convolutional Neural Network Modification Method

Syed Kashif Badshah Syed; Noor Badshah

Authors

Syed Kashif Badshah Syed University of Engineering and technology peshawar
Noor Badshah

Keywords:

Image Classification, Fuzzy Membership, VGG-19 Modified Model

Abstract

The COVID-19 positive, tuberculosis and pneumonia, share the trait of being able to be identified using radiological investigations, such as Chest X-ray (CXR) images. This paper aims to distinguish between four classes, including tuberculosis (TB), COVID-19 positive, healthy, and pneumonia using CXR images. Many deep-learning models such as a Convolutional Neural Network (CNN) have been developed for the Classification of CXR images. Deep learning-based models such as CNN offer significant advantages over traditional methods in the classification of diseases like TB, COVID-19, pneumonia, and healthy states. They provide higher accuracy, automation, early detection, reduced subjectivity, and resource efficiency, ultimately leading to improved patient care and outcomes. However, well-liked CNNs are massive models that require a lot of data to achieve optimal accuracy. In this paper, we propose a new CNN model that can be used to distinguish between different classes of CXR images. This model proves to be effective in classifying different diseases such as pneumonia, COVID-19, and tuberculosis. This study has used 6326 CXR images dataset containing COVID-19 positive, tuberculosis, and pneumonia and has normal images. In this dataset, 80% of the CXR images are taken for the training purpose and 20% are taken for the validation purpose, of the proposed CNN model. The proposed CNN modified model with parameter adjustment as well as using categorical cross-entropy as a loss function obtains the highest classification accuracy of 98.51% with a precision, recall, and F1 score of 0.98, 0.985, and 0.98 respectively.

Author Biographies

Syed Kashif Badshah Syed, University of Engineering and technology peshawar

Department of Basic Sciences and Islamiat

University of Engineering and Technology,

Peshawar, Pakistan

Noor Badshah

_{Department of Basic Sciences and Islamist}

_{University of Engineering and Technology,}

_{Peshawar, Pakistan}

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