IJIGSP Vol. 17, No. 2, 8 Apr. 2025
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Stroke, CT Scan, Artificial Intelligence, bio-signals, 3D-CNN, GAN, SGAN
The objective of the research work is to detect stroke using CT scan images. In the research work an analysis of 3D CNN method for stroke detection is presented. The work also presents a new method of stroke detection using semi-supervised Adversarial Networks (SGAN).3D CNN is the traditional approach to any type of image classification problem. But being data-hungry, it becomes difficult to use them when data is scarce. High-quality medical data is difficult to find and hence alternative approaches seem worth approaching. The relatively new GANs can generate images like the training images, and its SGAN variant can use these generated images for training the classifier. We investigate the usefulness of SGANs comparatively with CNNs in this paper. The proposed SGAN method is compared with state of art methods in literature using accuracy, sensitivity and specificity. The SGAN method demonstrates an accuracy of 93%, Sensitivity of 100% and Specificity of 90%. For small data sets in medical imaging the proposed SGAN method exhibit an encouraging performance as compared to other methods using large datasets. In the research paper, we propose methodologies for detecting strokes by using 2 approaches: 3D CNNs and SGANs. The relatively new GANs can generate images like the training images, and its SGAN variant can use these generated images for training the classifier. We investigate the usefulness of SGANs comparatively with CNNs in this paper.
Archana Chaudhari, Atharva Rajadhyaksha, Sharvil Patil, Himanshu Pawar, "CNN and GAN Based Stroke Detection Using CT Scan Images", International Journal of Image, Graphics and Signal Processing(IJIGSP), Vol.17, No.2, pp. 94-105, 2025. DOI:10.5815/ijigsp.2025.02.06
[1]Subudhi A, Dash M, Sabut S. Automated segmentation and classification of brain stroke using expectation-maximization and random forest classifier. Biocybern. Biomed. Eng. 2020; 40:277–289.
[2]Kasabov N, Feigin V, Hou ZG, Chen Y, Liang L, Krishnamurthi R, Othman M, Parmar P. Evolving spiking neural networks for personalised modelling, classification and prediction of spatio-temporal patterns with a case study on stroke. Neurocomputing 2014; 134: 269–279.
[3]Shanthi D, Sahoo G, Saravanan N, Designing an artificial neural network model for the prediction of thrombo-embolic stroke. Int. J. Biom. Bioinform. 2009; 3: 10–18.
[4]Bentley P, Ganesalingam J, Jones ALC, Mahady K, Epton S, Rinne P, Sharma P, Halse O, Mehta A, Rueckert D. Prediction of stroke thrombolysis outcome using CT brain machine learning. NeuroImage Clin. 2014; 4:635–640.
[5]Khosla A, Cao Y, Lin CCY, Chiu HK, Hu J, Lee H. An integrated machine learning approach to stroke prediction. In Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, 24–28 July 2010; 183–192.
[6]Yu J, Kim D, Park H, Chon S, Cho K, Kim S, Yu S, Park S, Hong S. Semantic Analysis of NIH stroke scale using machine learning techniques. In Proceedings of the 2019 International Conference on Platform Technology and Service (PlatCon), Jeju, Korea, 28–30 January 2019; 82–86.
[7]Amini L, Azarpazhouh R, FarzadfarMT, Mousavi SA Jazaieri F, Khorvash F, Norouzi R, Toghianfar N. Prediction and control of stroke by data mining. Int. J. Prev. Med. 2013; 4:245–249.
[8]Karthik R, Menaka R, Johnson A Anand Sundar. Neuroimaging and deep learning for brain stroke detection - A review of recent advancements and future prospects.Computer Methods and Programs in Biomedicine,2020; 105:728.
[9]Kamnitsas K, Ledig C, Newcombe VF, Simpson JP, Kane AD, Menon DK, Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation, Med. Image Anal. 2017; 36:61–78.
[10]Lisowska, A, O’Neil A, Dilys V, Daykin M, Beveridge E, Muir K. Context-aware convolutional neural networks for stroke sign detection in non-contrast CT scans, Commun. Comput. Inf. Sci. Med. 2017: 723.
[11]Chin CL, Lin BJ, Wu GR, Weng TC, Yang CS, Su RC, An automated early ischemic stroke detection system using CNN deep learning algorithm, 2017 IEEE 8th International Conference on Awareness Science and Technology (ICAST), 2017.
[12]Nielsen A, Hansen MB, Tietze A, Mouridsen K, Prediction of tissue outcome and assessment of treatment effect in acute ischemic stroke using deep learning, Stroke 2018; 49: 1394–1401.
[13]Shinohara Y, Takahashi N, Lee Y, Ohmura T, Kinoshita T, Development of a deep learning model to identify hyperdense MCA sign in patients with acute ischemic stroke, Jpn. J. Radiol. 2019; 38:112–117.
[14]Barman A, Inam ME, Lee S, Savitz S, Sheth S, Giancardo L, Determining ischemic stroke from CT-angiography imaging using symmetry-sensitive convolutional networks, 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019), 2019.
[15]Cho J, Park KS, Karki M, Lee E, Ko S, Kim J., Improving sensitivity on identification and delineation of intracranial hemorrhage lesion using cascaded deep learning models, J. Digit. Imaging 2019; 32:450–461.
[16]Jinlong H, Yuezhen K, Bin L, Lijie C, Shoubin D, and Ping L. A Multichannel 2D Convolutional Neural Network Model for Task-Evoked fMRI Data Classification,Computational Intelligence and Neuroscience, 2019.
[17]Kitsuchart P, Suchat T, and Supawit V, Semi-supervised learning with deep convolutional generative adversarial networks for canine red blood cells morphology classification Multimed Tools Appl 2020; 79: 34209–34226.
[18]https://www.kaggle.com/datasets/afridirahman/brain-stroke-ct-image-dataset. Accessed in Nov. 2021.