Context aware machine learning techniques for brain tumor classification and detection – A review
Background: Machine learning has tremendous potential in acute medical care, particularly in the field of precise medical diagnosis, prediction, and classification of brain tumors. Malignant gliomas, due to their aggressive growth and dismal prognosis, stand out among various brain tumor types. Rece...
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| Format: | Article |
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Elsevier
2025-01-01
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| Series: | Heliyon |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405844025002154 |
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| author | Usman Amjad Asif Raza Muhammad Fahad Doaa Farid Adnan Akhunzada Muhammad Abubakar Hira Beenish |
| author_facet | Usman Amjad Asif Raza Muhammad Fahad Doaa Farid Adnan Akhunzada Muhammad Abubakar Hira Beenish |
| author_sort | Usman Amjad |
| collection | DOAJ |
| description | Background: Machine learning has tremendous potential in acute medical care, particularly in the field of precise medical diagnosis, prediction, and classification of brain tumors. Malignant gliomas, due to their aggressive growth and dismal prognosis, stand out among various brain tumor types. Recent advancements in understanding the genetic abnormalities that underlie these tumors have shed light on their histo-pathological and biological characteristics, which support in better classification and prognosis. Objectives: This review aims to predict gene alterations and establish structured correlations among various tumor types, extending the prediction of genetic mutations and structures using the latest machine learning techniques. Specifically, it focuses on multi-modalities of Magnetic Resonance Imaging (MRI) and histopathology, utilizing Convolutional Neural Networks (CNN) for image processing and analysis. Methods: The review encompasses the most recent developments in MRI, and histology image processing methods across multiple tumor classes, including Glioma, Meningioma, Pituitary, Oligodendroglioma, and Astrocytoma. It identifies challenges in tumor classification, segmentation, datasets, and modalities, employing various neural network architectures. A competitive analysis assesses the performance of CNN. Furthermore it also implies K-MEANS clustering to predict Genetic structure, Genes Clusters prediction and Molecular Alteration of various types and grades of tumors e.g. Glioma, Meningioma, Pituitary, Oligodendroglioma, and Astrocytoma. Results: CNN and KNN structures, with their ability to extract highlights in image-based information, prove effective in tumor classification and segmentation, surmounting challenges in image analysis. Competitive analysis reveals that CNN and outperform others algorithms on publicly available datasets, suggesting their potential for precise tumor diagnosis and treatment planning. Conclusion: Machine learning, especially through CNN and SVM algorithms, demonstrates significant potential in the accurate diagnosis and classification of brain tumors based on imaging and histo-pathological data. Further advancements in this area hold promise for improving the accuracy and efficiency of intra-operative tumor diagnosis and treatment. |
| format | Article |
| id | doaj-art-6d581831ea8e4ad88cde8e20655ec5cf |
| institution | Kabale University |
| issn | 2405-8440 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Heliyon |
| spelling | doaj-art-6d581831ea8e4ad88cde8e20655ec5cf2025-02-02T05:28:20ZengElsevierHeliyon2405-84402025-01-01112e41835Context aware machine learning techniques for brain tumor classification and detection – A reviewUsman Amjad0Asif Raza1Muhammad Fahad2Doaa Farid3Adnan Akhunzada4Muhammad Abubakar5Hira Beenish6NED University of Engineering and Technology, Karachi, PakistanSir Syed University of Engineering and Technology, Karachi, PakistanKarachi Institute of Economics and Technology, Karachi, Pakistan; Corresponding author.College of Business, UDST, QatarCollege of Computing and IT, University of Doha for Science and Technology, QatarMuhammad Nawaz Shareef University of Engineering and Technology, Multan, PakistanKarachi Institute of Economics and Technology, Karachi, PakistanBackground: Machine learning has tremendous potential in acute medical care, particularly in the field of precise medical diagnosis, prediction, and classification of brain tumors. Malignant gliomas, due to their aggressive growth and dismal prognosis, stand out among various brain tumor types. Recent advancements in understanding the genetic abnormalities that underlie these tumors have shed light on their histo-pathological and biological characteristics, which support in better classification and prognosis. Objectives: This review aims to predict gene alterations and establish structured correlations among various tumor types, extending the prediction of genetic mutations and structures using the latest machine learning techniques. Specifically, it focuses on multi-modalities of Magnetic Resonance Imaging (MRI) and histopathology, utilizing Convolutional Neural Networks (CNN) for image processing and analysis. Methods: The review encompasses the most recent developments in MRI, and histology image processing methods across multiple tumor classes, including Glioma, Meningioma, Pituitary, Oligodendroglioma, and Astrocytoma. It identifies challenges in tumor classification, segmentation, datasets, and modalities, employing various neural network architectures. A competitive analysis assesses the performance of CNN. Furthermore it also implies K-MEANS clustering to predict Genetic structure, Genes Clusters prediction and Molecular Alteration of various types and grades of tumors e.g. Glioma, Meningioma, Pituitary, Oligodendroglioma, and Astrocytoma. Results: CNN and KNN structures, with their ability to extract highlights in image-based information, prove effective in tumor classification and segmentation, surmounting challenges in image analysis. Competitive analysis reveals that CNN and outperform others algorithms on publicly available datasets, suggesting their potential for precise tumor diagnosis and treatment planning. Conclusion: Machine learning, especially through CNN and SVM algorithms, demonstrates significant potential in the accurate diagnosis and classification of brain tumors based on imaging and histo-pathological data. Further advancements in this area hold promise for improving the accuracy and efficiency of intra-operative tumor diagnosis and treatment.http://www.sciencedirect.com/science/article/pii/S2405844025002154CNNMRISurvival predictionTumor segmentationHistologyDeep learning |
| spellingShingle | Usman Amjad Asif Raza Muhammad Fahad Doaa Farid Adnan Akhunzada Muhammad Abubakar Hira Beenish Context aware machine learning techniques for brain tumor classification and detection – A review Heliyon CNN MRI Survival prediction Tumor segmentation Histology Deep learning |
| title | Context aware machine learning techniques for brain tumor classification and detection – A review |
| title_full | Context aware machine learning techniques for brain tumor classification and detection – A review |
| title_fullStr | Context aware machine learning techniques for brain tumor classification and detection – A review |
| title_full_unstemmed | Context aware machine learning techniques for brain tumor classification and detection – A review |
| title_short | Context aware machine learning techniques for brain tumor classification and detection – A review |
| title_sort | context aware machine learning techniques for brain tumor classification and detection a review |
| topic | CNN MRI Survival prediction Tumor segmentation Histology Deep learning |
| url | http://www.sciencedirect.com/science/article/pii/S2405844025002154 |
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