Cover Image

Keras Implementation in Detecting Intracranial Hemorrhage and Multiclass Classification of Subtypes via Transfer Learning and Classifiers Selection

The development of deep neural networks for medical imaging applications, especially the diagnosis of intracranial hemorrhage (ICH) from CT scans, is greatly aided by machine learning frameworks such as Keras. This work investigates a pipeline that uses Keras' neural modules to distinguish betw...

Full description

Saved in:
Bibliographic Details
Main Authors: Amiir Haamzah, Mohamed Ismail, Anwar, P. P. Abdul Majeed
Format: Article
Language:English
Published: Penerbit UMP 2024
Subjects:
RS Pharmacy and materia medica
TS Manufactures
Online Access:http://umpir.ump.edu.my/id/eprint/43004/1/Keras%20Implementation%20in%20Detecting%20Intracranial%20Hemorrhage.pdf
http://umpir.ump.edu.my/id/eprint/43004/
https://doi.org/10.15282/mekatronika.v6i2.11358
https://doi.org/10.15282/mekatronika.v6i2.11358
Tags: Add Tag
No Tags, Be the first to tag this record!
id my.ump.umpir.43004
record_format eprints
spelling my.ump.umpir.430042024-12-03T02:33:35Z http://umpir.ump.edu.my/id/eprint/43004/ Keras Implementation in Detecting Intracranial Hemorrhage and Multiclass Classification of Subtypes via Transfer Learning and Classifiers Selection Amiir Haamzah, Mohamed Ismail Anwar, P. P. Abdul Majeed RS Pharmacy and materia medica TS Manufactures The development of deep neural networks for medical imaging applications, especially the diagnosis of intracranial hemorrhage (ICH) from CT scans, is greatly aided by machine learning frameworks such as Keras. This work investigates a pipeline that uses Keras' neural modules to distinguish between CT scans of the normal head and those with ICH. Transfer learning models are then used to categorize ICH subtypes. An extensive analysis of current research and techniques demonstrates the effectiveness of deep learning in medical imaging and emphasizes how AI may improve radiologists' diagnostic precision. Using windowing techniques to improve diagnostic features, the study preprocesses pictures from the RSNA Intracranial Hemorrhage Detection dataset. The study assesses performance indicators such classification accuracy using SVM, k-NN, and Random Forest classifiers combined with built-in models from Keras, such as Xception and DenseNet. Findings show that the Xception-SVM pipeline performs exceptionally well in binary classification tasks, achieving 76.33% accuracy, while DenseNet201-SVM performs well in multiclass classification, achieving 60% accuracy. These results highlight how crucial it is to choose the right pipelines for certain classification jobs in order to achieve the best results possible when using medical image analysis. In order to improve diagnostic precision in identifying cerebral hemorrhages, future research directions include increasing classifier performance, investigating sophisticated preprocessing techniques, and fine-tuning models. Penerbit UMP 2024-09-30 Article PeerReviewed pdf en cc_by_nc_4 http://umpir.ump.edu.my/id/eprint/43004/1/Keras%20Implementation%20in%20Detecting%20Intracranial%20Hemorrhage.pdf Amiir Haamzah, Mohamed Ismail and Anwar, P. P. Abdul Majeed (2024) Keras Implementation in Detecting Intracranial Hemorrhage and Multiclass Classification of Subtypes via Transfer Learning and Classifiers Selection. Mekatronika - Journal of Intelligent Manufacturing & Mechatronics, 6 (2). pp. 20-25. ISSN 2637-0883. (Published) https://doi.org/10.15282/mekatronika.v6i2.11358 https://doi.org/10.15282/mekatronika.v6i2.11358
institution Universiti Malaysia Pahang Al-Sultan Abdullah
building UMPSA Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang Al-Sultan Abdullah
content_source UMPSA Institutional Repository
url_provider http://umpir.ump.edu.my/
language English
topic RS Pharmacy and materia medica
TS Manufactures
spellingShingle RS Pharmacy and materia medica
TS Manufactures
Amiir Haamzah, Mohamed Ismail
Anwar, P. P. Abdul Majeed
Keras Implementation in Detecting Intracranial Hemorrhage and Multiclass Classification of Subtypes via Transfer Learning and Classifiers Selection
description The development of deep neural networks for medical imaging applications, especially the diagnosis of intracranial hemorrhage (ICH) from CT scans, is greatly aided by machine learning frameworks such as Keras. This work investigates a pipeline that uses Keras' neural modules to distinguish between CT scans of the normal head and those with ICH. Transfer learning models are then used to categorize ICH subtypes. An extensive analysis of current research and techniques demonstrates the effectiveness of deep learning in medical imaging and emphasizes how AI may improve radiologists' diagnostic precision. Using windowing techniques to improve diagnostic features, the study preprocesses pictures from the RSNA Intracranial Hemorrhage Detection dataset. The study assesses performance indicators such classification accuracy using SVM, k-NN, and Random Forest classifiers combined with built-in models from Keras, such as Xception and DenseNet. Findings show that the Xception-SVM pipeline performs exceptionally well in binary classification tasks, achieving 76.33% accuracy, while DenseNet201-SVM performs well in multiclass classification, achieving 60% accuracy. These results highlight how crucial it is to choose the right pipelines for certain classification jobs in order to achieve the best results possible when using medical image analysis. In order to improve diagnostic precision in identifying cerebral hemorrhages, future research directions include increasing classifier performance, investigating sophisticated preprocessing techniques, and fine-tuning models.
format Article
author Amiir Haamzah, Mohamed Ismail
Anwar, P. P. Abdul Majeed
author_facet Amiir Haamzah, Mohamed Ismail
Anwar, P. P. Abdul Majeed
author_sort Amiir Haamzah, Mohamed Ismail
title Keras Implementation in Detecting Intracranial Hemorrhage and Multiclass Classification of Subtypes via Transfer Learning and Classifiers Selection
title_short Keras Implementation in Detecting Intracranial Hemorrhage and Multiclass Classification of Subtypes via Transfer Learning and Classifiers Selection
title_full Keras Implementation in Detecting Intracranial Hemorrhage and Multiclass Classification of Subtypes via Transfer Learning and Classifiers Selection
title_fullStr Keras Implementation in Detecting Intracranial Hemorrhage and Multiclass Classification of Subtypes via Transfer Learning and Classifiers Selection
title_full_unstemmed Keras Implementation in Detecting Intracranial Hemorrhage and Multiclass Classification of Subtypes via Transfer Learning and Classifiers Selection
title_sort keras implementation in detecting intracranial hemorrhage and multiclass classification of subtypes via transfer learning and classifiers selection
publisher Penerbit UMP
publishDate 2024
url http://umpir.ump.edu.my/id/eprint/43004/1/Keras%20Implementation%20in%20Detecting%20Intracranial%20Hemorrhage.pdf
http://umpir.ump.edu.my/id/eprint/43004/
https://doi.org/10.15282/mekatronika.v6i2.11358
https://doi.org/10.15282/mekatronika.v6i2.11358
_version_ 1822924775115194368
score 13.235367

Similar Items