Evaluation of Diagnostic Accuracy of Machine Learning Algorithm for Computer Aided Diagnosis (CAD) of Space Occupying Lesions in Computed Tomography (CT) Images of Brain
Journal Title: International Journal of Orthopaedics Traumatology & Surgical Sciences - Year 2018, Vol 4, Issue 2
Abstract
Objectives-Objective of this project was to formulate a low cost and userfriendly SOL detection computational model through machine learning and test its diagnostic efficacy through retrospective evaluation of radiologist confirmed normal and abnormal CT images. Materials & Methods-The neural network has been created in the Wolfram language, using our own dataset. A total of 249 images were obtained from a CT scan center’s human brain data. A database of 89 normal and 76 SOL images from 128 slice CT scan machines was used for formulating the model. The test trial was done using 50 different normal and 34 different abnormal images. Results-The sensitivity of ability to identify abnormal images was 97% and the specificity was 92%. Only one out of 34 images with tumor was reported wrongly and 4 normal images out of 50 normal test images were wrongly identified by the CAD system. The software was able to achieve a diagnostic accuracy higher than the confidence interval of 95% set for the diagnostic test. Conclusion - The CAD system is novel and innovative as it is a simple, lowcost and user-friendly computational model which can be used by doctors on their office PCs.
Authors and Affiliations
Rushank Goyal, Swati Goyal, Lovely Kaushal
Keywords
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