The Implications of Varying Batch-Size in the Classification of Patch-Based Lung Nodules Using Convolutional Neural Network Architecture on Computed Tomography Images
Bushara A. R.
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Noorul Islam Center for Higher Education, Kanyakumari, Tamil Nadu, India
Paper #9056 received 11 Jan 2024; revised manuscript received 5 Feb 2024; accepted for publication 12 Feb 2024; published online 4 Mar 2024.
DOI: 10.18287/JBPE24.10.010305
Abstract
In recent years, the research on deep Convolutional Neural Networks (CNNs) has led to remarkable advancements in image categorization and segmentation. This paper presents a new Computer-Aided Detection (CAD) system utilizing convolutional neural networks and Computed Tomography (CT) image segmentation techniques to address the same problem of diagnostic lung nodule detection in low-dose CT scans. To simplify the approach, the system utilizes CNN for the classification of the malignant nodule. Specifically, divide each CT scan into several patches, with nodules and the remainder of the image falling into separate groups. Utilizing CT images from the Lung Image Database Consortium and Image Database Resource Initiative, the CNN models are evaluated. The overall accuracy metric measures the percentage of correctly classified instances (both benign and malignant) out of the total number of instances. From the results, it is observed that the overall accuracy generally ranges from 93% to 96% for different batch sizes. According to the findings, the most advanced model can achieve a detection accuracy of 96% with 256 batch size.
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