Multi-class disease detection using deep learning and human brain medical imaging

Medical imaging and deep learning methods have significantly improved the early detection of brain diseases like tumors and Ischemic stroke with higher accuracy. Machine learning methods especially neural-network based algorithms have shown huge success in medical image analysis for variety of tasks...

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Main Authors: Yousaf, Fatima, Iqbal, Sajid, Fatima, Nosheen, Kousar, Tanzeela, Mohd. Rahim, Mohd. Shafry
Format: Article
Published: Elsevier Ltd 2023
Subjects:
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author Yousaf, Fatima
Iqbal, Sajid
Fatima, Nosheen
Kousar, Tanzeela
Mohd. Rahim, Mohd. Shafry
author_facet Yousaf, Fatima
Iqbal, Sajid
Fatima, Nosheen
Kousar, Tanzeela
Mohd. Rahim, Mohd. Shafry
author_sort Yousaf, Fatima
collection ePrints
description Medical imaging and deep learning methods have significantly improved the early detection of brain diseases like tumors and Ischemic stroke with higher accuracy. Machine learning methods especially neural-network based algorithms have shown huge success in medical image analysis for variety of tasks including the detection, segmentation and classification of brain tumors and Ischemic stroke. Usually, these models address one problem at a time which is considered as Artificial Weak Intelligence (AWI). There is the need to develop methods that can push the research towards strong or Artificial General AI (AGI) where a single model can solve multiple tasks. In this work, we propose convolutional neural network based integrated model to detect and classify two brain diseases simultaneously i.e., tumors and Ischemic stroke. For this, a new dataset is created by merging two open-source datasets: BRATS 2015 and ISLES 2015. The designed network is an enhancement of encoder-decoder architecture based UNET where feature maps from one encoder block are fused with output of following encoder block to keep low-level fine-grained information intact and distinguish the overlapping features during encoding process in addition to UNET skip connections. The dataset is partitioned into training and validation sets on the ratio of 80:20 respectively with proportionate image inclusion in each training batch to address class imbalance issue. Average accuracy obtained through proposed model is 99.56%, 99.99% specificity, 99.59% precision and F1- score 99.57%. Obtained performance scores shows the usability of proposed feature fusion mechanism for multi-disease detection.
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spelling utm.eprints-1055132024-04-30T08:09:12Z http://eprints.utm.my/105513/ Multi-class disease detection using deep learning and human brain medical imaging Yousaf, Fatima Iqbal, Sajid Fatima, Nosheen Kousar, Tanzeela Mohd. Rahim, Mohd. Shafry QA75 Electronic computers. Computer science Medical imaging and deep learning methods have significantly improved the early detection of brain diseases like tumors and Ischemic stroke with higher accuracy. Machine learning methods especially neural-network based algorithms have shown huge success in medical image analysis for variety of tasks including the detection, segmentation and classification of brain tumors and Ischemic stroke. Usually, these models address one problem at a time which is considered as Artificial Weak Intelligence (AWI). There is the need to develop methods that can push the research towards strong or Artificial General AI (AGI) where a single model can solve multiple tasks. In this work, we propose convolutional neural network based integrated model to detect and classify two brain diseases simultaneously i.e., tumors and Ischemic stroke. For this, a new dataset is created by merging two open-source datasets: BRATS 2015 and ISLES 2015. The designed network is an enhancement of encoder-decoder architecture based UNET where feature maps from one encoder block are fused with output of following encoder block to keep low-level fine-grained information intact and distinguish the overlapping features during encoding process in addition to UNET skip connections. The dataset is partitioned into training and validation sets on the ratio of 80:20 respectively with proportionate image inclusion in each training batch to address class imbalance issue. Average accuracy obtained through proposed model is 99.56%, 99.99% specificity, 99.59% precision and F1- score 99.57%. Obtained performance scores shows the usability of proposed feature fusion mechanism for multi-disease detection. Elsevier Ltd 2023 Article PeerReviewed Yousaf, Fatima and Iqbal, Sajid and Fatima, Nosheen and Kousar, Tanzeela and Mohd. Rahim, Mohd. Shafry (2023) Multi-class disease detection using deep learning and human brain medical imaging. Biomedical Signal Processing and Control, 85 (NA). NA-NA. ISSN 1746-8094 http://dx.doi.org/10.1016/j.bspc.2023.104875 DOI : 10.1016/j.bspc.2023.104875
spellingShingle QA75 Electronic computers. Computer science
Yousaf, Fatima
Iqbal, Sajid
Fatima, Nosheen
Kousar, Tanzeela
Mohd. Rahim, Mohd. Shafry
Multi-class disease detection using deep learning and human brain medical imaging
title Multi-class disease detection using deep learning and human brain medical imaging
title_full Multi-class disease detection using deep learning and human brain medical imaging
title_fullStr Multi-class disease detection using deep learning and human brain medical imaging
title_full_unstemmed Multi-class disease detection using deep learning and human brain medical imaging
title_short Multi-class disease detection using deep learning and human brain medical imaging
title_sort multi class disease detection using deep learning and human brain medical imaging
topic QA75 Electronic computers. Computer science
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AT fatimanosheen multiclassdiseasedetectionusingdeeplearningandhumanbrainmedicalimaging
AT kousartanzeela multiclassdiseasedetectionusingdeeplearningandhumanbrainmedicalimaging
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