A deep convolutional neural network model for medical data classification from computed tomography images

Abstract

Abstract Machine learning provides powerful techniques for several applications, including automated disease diagnosis through medical image classification. Recently, many studies reported that deep learning approaches have demonstrated significant performance and accuracy improvements over shallow learning techniques. The deep learning approaches have been used in many problems related to disease diagnoses, such as thyroid diagnosis, diabetic retinopathy detection, foetal localization, and breast cancer detection. Many deep learning methods have been reported in the recent past that uses medical images from various sources, such as healthcare providers and open data initiatives, and reported significant improvement in terms of precision, recall, and accuracy. This paper proposes a framework incorporating deep convolutional neural networks and an enhanced feature extraction technique for classifying medical data. To show the real‐world usability of the proposed approach, it has been used for the classification of COVID‐19 images from computed tomography scans. The experimental results show that the proposed approach outperformed some of the chosen baselines and obtained an accuracy of 98.91%, comparable with already reported accuracies.

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