Early Detection of Hemorrhagic Stroke Using a Lightweight Deep Learning Neural Network Model

Vamsi, Bandi; Bhattacharyya, Debnath; Midhunchakkravarthy, Divya; Kim, Jung-yoon

doi:10.18280/ts.380616

Cited by 9 publications

(10 citation statements)

References 25 publications

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“…Vamsi et al achieved an accuracy of 97.81% using VGG-16 and random forest, representing another successful hybrid approach. 10…”

Section: Discussionmentioning

confidence: 99%

Comparative analysis of deep learning techniques for accurate stroke detection

Kaewlek,

Sitinwan,

Lueangaroon

et al. 2024

JAMS

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Background: The traditional diagnosis of strokes through computed tomography (CT) heavily relies on radiologists’ expertise for accurate interpretation. However, the increasing demand for this critical task exceeds the available radiologist workforce, necessitating innovative solutions. This research addresses this challenge by introducing deep learning techniques to enhance the initial screening of stroke cases, thereby augmenting radiologists’ diagnostic capabilities. Objective: This study aims to compare four techniques for classifying stroke lesions in CT images. Materials and methods: Four distinct models-CNN-2-Model, LeNet, GoogleNet, and VGG-16-were trained using a dataset comprising 1,636 CT images, including 1,111 normal brain images and 525 stroke images. Seventy percent of the images were used to train the most effective deep learning model, and subsequently, these images were utilized to evaluate the performance of each model. The evaluation involved assessing accuracy, precision, sensitivity, specificity, F1 score, false positive rate, and AUC. Results: The evaluation process included a comprehensive statistical analysis of the models’ prediction results. The findings revealed that VGG-16 emerged as the top-performing deep learning model, achieving an impressive accuracy of 0.969, precision of 0.952, sensitivity of 0.952, specificity of 0.978, F1 score of 0.952, false positive rate of 0.022, and AUC of 0.965. Conclusion: In conclusion, deep learning techniques, particularly the VGG-16 model, demonstrate significant promise in enhancing the accuracy of stroke lesion classification in CT images. These findings underscore the potential of leveraging advanced technologies to address the growing challenges in stroke diagnosis and pave the way for more efficient and accessible healthcare solutions.

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“…Vamsi et al achieved an accuracy of 97.81% using VGG-16 and random forest, representing another successful hybrid approach. 10…”

Section: Discussionmentioning

confidence: 99%

Comparative analysis of deep learning techniques for accurate stroke detection

Kaewlek,

Sitinwan,

Lueangaroon

et al. 2024

JAMS

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“…VGG is a multi-layered deep CNN architecture [5]. The "deep" refers to the number of layers, with VGG-16 or VGG-19 having 16 or 19 convolutional layers, respectively.…”

Section: Visual Geometry Group Network (Vggnet)mentioning

confidence: 99%

“…CT [5], [6], [9], [11], [14 -42], [124], [127], [130] MRI [4], [7], [9], [12], [14 -18], [25], [43 -68], [127], [131 -133] Text [3], [69 -72], [128] EEG [8], [10], [73 -79], [125], [126] Ultrasound image…”

Section: Mode Of Data Collection Referencesmentioning

confidence: 99%

Deep Learning and Artificial Intelligence in Action (2019–2023): A Review on Brain Stroke Detection, Diagnosis, and Intelligent Post-Stroke Rehabilitation Management

Chaki,

Woźniak

2024

IEEE Access

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Brain stroke is a complicated disease that is one of the foremost reasons of long-term debility and mortality. Because of breakthroughs in Deep Learning (DL) and Artificial Intelligence (AI) which enable the automated detection and diagnosis of brain stroke as well as intelligently assisting post-brain stroke patients for rehabilitation, is more favorable than a manual diagnosis. Many publications on automated brain stroke detection, diagnosis, and robotic management using DL and AI approaches are now being published. This review provides a study of the detection, diagnosis of brain stroke and robotic management techniques of post-brain stroke rehabilitation from six different perspectives, namely, brain stroke datasets and modalities of brain stroke data collection, pre-processing approaches, DL-based detection and diagnosis of brain stroke, Al-based intelligent post brain stroke rehabilitation assistant, and performance measures. It also examines the conclusions and the consequences of the findings. There are also three ongoing research challenges in the fields of brain stroke detection and diagnosis, as well as post-brain stroke robotic treatment. For this investigation, 130 key papers from the Scopus, PubMed and Web of Science archives were chosen after a comprehensive screening method. This study gives a comprehensive overview of brain stroke detection and post-brain stroke robotic management strategies that may be useful to the scientist's community working in the field of automatic brain stroke detection and robotic rehabilitation management.

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“…Vamsi et al used a lightweight CNN with a Random Forest classifier to identify stroke regions of the brain, achieving an accuracy of 97.81% [ 28 ].…”

Section: Related Workmentioning

confidence: 99%

“…From the above work [ 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 ], it is observed that most researchers have proposed segmentation work for identifying regions of cardiomegaly and other diseases using heavyweight convolution networks. Existing lightweight work [ 28 ] has primarily used specific ML algorithms for extracting pixel values from data frames. To address these limitations, we propose an ensemble-based voting classifier model for extracting pixel values from data frames to identify enlarged regions more effectively.…”

Section: Related Workmentioning

confidence: 99%

An Efficient, Lightweight, Tiny 2D-CNN Ensemble Model to Detect Cardiomegaly in Heart CT Images

Doppala,

Al Bataineh,

Vamsi

2023

JPM

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Cardiomegaly is a significant global health concern, especially in developing nations. Although advanced clinical care is available for newly diagnosed patients, many in resource-limited regions face late diagnoses and consequent increased mortality. This challenge is accentuated by a scarcity of radiography equipment and radiologists. Hence, we propose the development of a computer-aided diagnostic (CAD) system, specifically a lightweight, tiny 2D-CNN ensemble model, to facilitate early detection and, potentially, reduce mortality rates. Deep learning, with its subset of convolutional neural networks (CNN), has shown potential in visual applications, especially in medical image diagnosis. However, traditional deep CNNs often face compatibility issues with object-oriented human factor technology. Our proposed model aims to bridge this gap. Using CT scan images sourced from the Mendeley data center, our tiny 2D-CNN ensemble learning model achieved an accuracy of 96.32%, offering a promising tool for efficient and accurate cardiomegaly detection.

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Early Detection of Hemorrhagic Stroke Using a Lightweight Deep Learning Neural Network Model

Cited by 9 publications

References 25 publications

Comparative analysis of deep learning techniques for accurate stroke detection

Comparative analysis of deep learning techniques for accurate stroke detection

Deep Learning and Artificial Intelligence in Action (2019–2023): A Review on Brain Stroke Detection, Diagnosis, and Intelligent Post-Stroke Rehabilitation Management

An Efficient, Lightweight, Tiny 2D-CNN Ensemble Model to Detect Cardiomegaly in Heart CT Images

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