In this research report, various Machine Learning (ML) models are discussed for the purpose of detecting brain anomalies like tumors. In the first step, we review previous work that uses Deep Learning (DL) to classify...In this research report, various Machine Learning (ML) models are discussed for the purpose of detecting brain anomalies like tumors. In the first step, we review previous work that uses Deep Learning (DL) to classify and detect brain tumors. Next, we present a detailed analysis of the ML methods in tabular form to address the brain tumor morphology, accessible datasets, segmentation, extraction, and classification using DL, and ML models. Finally, we summarize all relevant material for tumor detection, including the merits, limitations and future directions. In this study, it is found that employing DL-based and hybrid-based metaheuristic approaches proves to be more effective in accurately segmenting brain tumors, compared to the conventional methods. However, the brain tumor segmentation using ML models suffers from drawbacks due to limited labelled data, variability in tumor appearance, computational memory requirements, transparency in models, and difficulty in integration into clinical workflows. By pursuing techniques such as Data Augmentation, Pre-training, Active-learning, Multimodal fusion, Hardware acceleration, and Clinical integration, researchers and developers can overcome the bottlenecks and enhance the accuracy, efficiency, and clinical utility of ML-based brain tumor segmentation models.展开更多
文摘In this research report, various Machine Learning (ML) models are discussed for the purpose of detecting brain anomalies like tumors. In the first step, we review previous work that uses Deep Learning (DL) to classify and detect brain tumors. Next, we present a detailed analysis of the ML methods in tabular form to address the brain tumor morphology, accessible datasets, segmentation, extraction, and classification using DL, and ML models. Finally, we summarize all relevant material for tumor detection, including the merits, limitations and future directions. In this study, it is found that employing DL-based and hybrid-based metaheuristic approaches proves to be more effective in accurately segmenting brain tumors, compared to the conventional methods. However, the brain tumor segmentation using ML models suffers from drawbacks due to limited labelled data, variability in tumor appearance, computational memory requirements, transparency in models, and difficulty in integration into clinical workflows. By pursuing techniques such as Data Augmentation, Pre-training, Active-learning, Multimodal fusion, Hardware acceleration, and Clinical integration, researchers and developers can overcome the bottlenecks and enhance the accuracy, efficiency, and clinical utility of ML-based brain tumor segmentation models.
