ePrints@IIScePrints@IISc Home | About | Browse | Latest Additions | Advanced Search | Contact | Help

A novel variant of deep convolutional neural network for classification of ovarian tumors using CT images

Kodipalli, A and Devi, SV and Dasar, S and Ismail, T (2023) A novel variant of deep convolutional neural network for classification of ovarian tumors using CT images. In: Computers and Electrical Engineering, 109 .

[img] PDF
com_ele_eng_109_2023.pdf - Published Version
Restricted to Registered users only

Download (3MB) | Request a copy
Official URL: https://doi.org/10.1016/j.compeleceng.2023.108758


Deep Learning models have shown tremendously impressive performance on image classification tasks. In the medical imaging domain, progress has been made in obtaining high-quality data for analysis and using state-of-the- art artificial intelligence algorithms for solving complex problems and providing answers to key questions using data. One such problem that is of crucial importance and interest to medical researchers is to classify tumors into two categories benign and malignant. This research work focuses on proposing a novel variation of CNN architecture and a comparison of the performances of state-of-the-art ImageNet Large Scale Visual Recognition Challenge (ILSVRC) winning architectures for the task of classifying ovarian tumors by training and evaluating images on a dataset of ovarian CT scan images with the help of cloud services such as Google Cloud Platform. The proposed architecture has attained an accuracy of 97.53% and outperformed the existing CNN variants.

Item Type: Journal Article
Publication: Computers and Electrical Engineering
Publisher: Elsevier Ltd
Additional Information: The copyright for this article belongs to the Elsevier Ltd.
Keywords: Big data; Cloud computing; Computational intelligent framework; Deep neural networks; Google cloud; ILSVRC; Ovarian tumors.
Department/Centre: Division of Electrical Sciences > Computer Science & Automation
Date Deposited: 17 Jul 2023 11:08
Last Modified: 17 Jul 2023 11:08
URI: https://eprints.iisc.ac.in/id/eprint/82449

Actions (login required)

View Item View Item