Multi‐branch sustainable convolutional neural network for disease classification

Author:

Naz Maria1ORCID,Shah Munam Ali1ORCID,Khattak Hasan Ali2ORCID,Wahid Abdul23ORCID,Asghar Muhammad Nabeel4,Rauf Hafiz Tayyab5ORCID,Khan Muhammad Attique6ORCID,Ameer Zoobia7ORCID

Affiliation:

1. Department of Computer Science COMSATS University Islamabad Islamabad Pakistan

2. School of Electrical Engineering & Computer Science (SEECS) National University of Sciences and Technology (NUST) 44500 Islamabad Pakistan

3. School of Computer Science University of Birmingham Dubai United Arab Emirates

4. Department of Computer Science Bahauddin Zakariya University Multan Pakistan

5. Centre for Smart Systems, AI and Cybersecurity Staffordshire University ST4 2DE Stoke‐on‐Trent UK

6. HITEC University Taxila Taxila Pakistan

7. Shaheed Benazir Bhutto Women University Peshawar Peshawar Pakistan

Abstract

AbstractPandemic and natural disasters are growing more often, imposing even more pressure on life care services and users. There are knowledge gaps regarding how to prevent disasters and pandemics. In recent years, after heart disease, corona virus disease‐19 (COVID‐19), brain stroke, and cancer are at their peak. Different machine learning and deep learning‐based techniques are presented to detect these diseases. Existing technique uses two branches that have been used for detection and prediction of disease accurately such as brain hemorrhage. However, existing techniques have been focused on the detection of specific diseases with double‐branches convolutional neural networks (CNNs). There is a need to develop a model to detect multiple diseases at the same time using computerized tomography (CT) scan images. We proposed a model that consists of 12 branches of CNN to detect the different types of diseases with their subtypes using CT scan images and classify them more accurately. We proposed multi‐branch sustainable CNN model with deep learning architecture trained on the brain CT hemorrhage, COVID‐19 lung CT scans and chest CT scans with subtypes of lung cancers. Feature extracted automatically from preprocessed input data and passed to classifiers for classification in the form of concatenated feature vectors. Six classifiers support vector machine (SVM), decision tree (DT), K‐nearest neighbor (K‐NN), artificial neural network (ANN), naïve Bayes (NB), linear regression (LR) classifiers, and three ensembles the random forest (RF), AdaBoost, gradient boosting ensembles were tested on our model for classification and prediction. Our model achieved the best results on RF on each dataset. Respectively, on brain CT hemorrhage achieved (99.79%) accuracy, on COVID‐19 lung CT scans achieved (97.61%), and on chest CT scans dataset achieved (98.77%).

Publisher

Wiley

Subject

Electrical and Electronic Engineering,Computer Vision and Pattern Recognition,Software,Electronic, Optical and Magnetic Materials

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1. Correction to “Multi‐Branch Sustainable Convolutional Neural Network for Disease Classification”;International Journal of Imaging Systems and Technology;2024-08-06

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