Open Access

ARTICLE

CVIP-Net: A Convolutional Neural Network-Based Model for Forensic Radiology Image Classification

Syeda Naila Batool, Ghulam Gilanie^*

Department of Artificial Intelligence, Faculty of Computing, The Islamia University of Bahawalpur, Bahawalpur, 63100, Pakistan

* Corresponding Author: Ghulam Gilanie. Email:

Computers, Materials & Continua 2023, 74(1), 1319-1332. https://doi.org/10.32604/cmc.2023.032121

Received 07 May 2022; Accepted 29 June 2022; Issue published 22 September 2022

Abstract

Automated and autonomous decisions of image classification systems have essential applicability in this modern age even. Image-based decisions are commonly taken through explicit or auto-feature engineering of images. In forensic radiology, auto decisions based on images significantly affect the automation of various tasks. This study aims to assist forensic radiology in its biological profile estimation when only bones are left. A benchmarked dataset Radiology Society of North America (RSNA) has been used for research and experiments. Additionally, a locally developed dataset has also been used for research and experiments to cross-validate the results. A Convolutional Neural Network (CNN)-based model named computer vision and image processing-net (CVIP-Net) has been proposed to learn and classify image features. Experiments have also been performed on state-of-the-art pertained models, which are alex_net, inceptionv_3, google_net, Residual Network (resnet)_50, and Visual Geometry Group (VGG)-19. Experiments proved that the proposed CNN model is more accurate than other models when panoramic dental x-ray images are used to identify age and gender. The specially designed CNN-based achieved results in terms of standard evaluation measures including accuracy (98.90%), specificity (97.99%), sensitivity (99.34%), and Area under the Curve (AUC)-value (0.99) on the locally developed dataset to detect age. The classification rates of the proposed model for gender estimation were 99.57%, 97.67%, 98.99%, and 0.98, achieved in terms of accuracy, specificity, sensitivity, and AUC-value, respectively, on the local dataset. The classification rates of the proposed model for age estimation were 96.80%, 96.80%, 97.03%, and 0.99 achieved in terms of accuracy, specificity, sensitivity, and AUC-value, respectively, on the RSNA dataset.

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