Neethi, A.S.Niyas, S.Kannath, S.K.Mathew, J.Anzar, A.M.Rajan, J.2026-02-042022Biomedical Signal Processing and Control, 2022, 76, , pp. -17468094https://doi.org/10.1016/j.bspc.2022.103720https://idr.nitk.ac.in/handle/123456789/22520Stroke is a cerebrovascular condition with a significant morbidity and mortality rate and causes physical disabilities for survivors. Once the symptoms are identified, it requires a time-critical diagnosis with the help of the most commonly available imaging techniques. Computed tomography (CT) scans are used worldwide for preliminary stroke diagnosis. It demands the expertise and experience of a radiologist to identify the stroke type, which is critical for initiating the treatment. This work attempts to gather those domain skills and build a model from CT scans to diagnose stroke. The non-contrast computed tomography (NCCT) scan of the brain comprises volumetric images or a 3D stack of image slices. So, a model that aims to solve the problem by targeting a 2D slice may fail to address the volumetric nature. We propose a 3D-based fully convolutional classification model to identify stroke cases from CT images that take into account the contextual longitudinal composition of volumetric data. We formulate a custom pre-processing module to enhance the scans and aid in improving the classification performance. Some of the significant challenges faced by 3D CNN are the less number of training samples, and the number of scans is mostly biased in favor of normal patients. In this work, the limitation of insufficient training volume and class imbalanced data have been rectified with the help of a strided slicing approach. A block-wise design was used to formulate the proposed network, with the initial part focusing on adjusting the dimensionality, at the same time retaining the features. Later on, the accumulated feature maps were effectively learned utilizing bundled convolutions and skip connections. The results of the proposed method were compared against 3D CNN stroke classification models on NCCT, various 3D CNN architectures on other brain imaging modalities, and 3D extensions of some of the classical CNN architectures. The proposed method achieved an improvement of 14.28% in the F1-score over the state-of-the-art 3D CNN stroke classification model. © 2022 Elsevier LtdBrain mappingClassification (of information)ConvolutionConvolutional neural networksDeep learningDiagnosisNetwork architectureVolumetric analysis3d convolutional neural networkClassification modelsComputed tomography scanConditionConvolutional neural networkMortality rateNon contrast computed tomographyPhysical disabilityStroke classificationComputerized tomographyArticlebrain hemorrhagebrain regioncerebrovascular accidentcomparative studycomputer assisted tomographycontrolled studyconvolutional neural networkdeep learningdisease classificationdisease severityfalse negative resultfalse positive resulthumanimaging algorithmneuroimagingresidual neural networkthree dimensional convolutional neural network