Automated Brain Tumor Classification System using Convolutional Neural Networks from MRI Images

Bala, Diponkor; Islam, Mohammad Anwarul; Hossain, Mohammad Ismail; Mynuddin, Mohammed; Hossain, Mohammad Alamgir; Hossain, Md. Shamim

doi:10.1109/iceet56468.2022.10007116

Cited by 9 publications

(4 citation statements)

References 16 publications

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“…This architecture incorporated activation functions, normalization techniques, pooling layers, and dropout mechanisms to counter overfitting. Remarkably, exceeding current state-of-the-art approaches, their model attained an outstanding accuracy rate of 97.7% [ 18 ].…”

Section: Related Workmentioning

confidence: 99%

Enhancing Automated Brain Tumor Detection Accuracy Using Artificial Intelligence Approaches for Healthcare Environments

Abdusalomov,

Rakhimov,

Karimberdiyev

et al. 2024

Bioengineering

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Medical imaging and deep learning models are essential to the early identification and diagnosis of brain cancers, facilitating timely intervention and improving patient outcomes. This research paper investigates the integration of YOLOv5, a state-of-the-art object detection framework, with non-local neural networks (NLNNs) to improve brain tumor detection’s robustness and accuracy. This study begins by curating a comprehensive dataset comprising brain MRI scans from various sources. To facilitate effective fusion, the YOLOv5 and NLNNs, K-means+, and spatial pyramid pooling fast+ (SPPF+) modules are integrated within a unified framework. The brain tumor dataset is used to refine the YOLOv5 model through the application of transfer learning techniques, adapting it specifically to the task of tumor detection. The results indicate that the combination of YOLOv5 and other modules results in enhanced detection capabilities in comparison to the utilization of YOLOv5 exclusively, proving recall rates of 86% and 83% respectively. Moreover, the research explores the interpretability aspect of the combined model. By visualizing the attention maps generated by the NLNNs module, the regions of interest associated with tumor presence are highlighted, aiding in the understanding and validation of the decision-making procedure of the methodology. Additionally, the impact of hyperparameters, such as NLNNs kernel size, fusion strategy, and training data augmentation, is investigated to optimize the performance of the combined model.

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Section: Related Workmentioning

confidence: 99%

Enhancing Automated Brain Tumor Detection Accuracy Using Artificial Intelligence Approaches for Healthcare Environments

Abdusalomov,

Rakhimov,

Karimberdiyev

et al. 2024

Bioengineering

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“…Another CNN model was designed by [ 45 ] and trained with a medical dataset acquired from Al-Kindi Hospital and Baghdad Medical City to classify skin lesions, obtaining accuracy of 89%. In [ 46 ], seven different types of skin problems were categorized, using a CNN.…”

Section: Literature Reviewmentioning

confidence: 99%

SkinLesNet: Classification of Skin Lesions and Detection of Melanoma Cancer Using a Novel Multi-Layer Deep Convolutional Neural Network

Azeem,

Kiani,

Mansouri

et al. 2023

Cancers

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Skin cancer is a widespread disease that typically develops on the skin due to frequent exposure to sunlight. Although cancer can appear on any part of the human body, skin cancer accounts for a significant proportion of all new cancer diagnoses worldwide. There are substantial obstacles to the precise diagnosis and classification of skin lesions because of morphological variety and indistinguishable characteristics across skin malignancies. Recently, deep learning models have been used in the field of image-based skin-lesion diagnosis and have demonstrated diagnostic efficiency on par with that of dermatologists. To increase classification efficiency and accuracy for skin lesions, a cutting-edge multi-layer deep convolutional neural network termed SkinLesNet was built in this study. The dataset used in this study was extracted from the PAD-UFES-20 dataset and was augmented. The PAD-UFES-20-Modified dataset includes three common forms of skin lesions: seborrheic keratosis, nevus, and melanoma. To comprehensively assess SkinLesNet’s performance, its evaluation was expanded beyond the PAD-UFES-20-Modified dataset. Two additional datasets, HAM10000 and ISIC2017, were included, and SkinLesNet was compared to the widely used ResNet50 and VGG16 models. This broader evaluation confirmed SkinLesNet’s effectiveness, as it consistently outperformed both benchmarks across all datasets.

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“…Recently, machine learning (ML) applications have witnessed unprecedented growth across various fields [5]- [7], revolutionizing the way tasks are performed and problems are addressed [8]- [10]. One notable domain where machine learning, particularly Deep Learning (DL), has made substantial contributions is in the realm of intrusion detection systems (IDS) [11].…”

Section: Introductionmentioning

confidence: 99%

Automatic Network Intrusion Detection System Using Machine learning and Deep learning

Mynuddin,

Khan,

Chowdhury

et al. 2024

Preprint

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Automated Brain Tumor Classification System using Convolutional Neural Networks from MRI Images

Cited by 9 publications

References 16 publications

Enhancing Automated Brain Tumor Detection Accuracy Using Artificial Intelligence Approaches for Healthcare Environments

Enhancing Automated Brain Tumor Detection Accuracy Using Artificial Intelligence Approaches for Healthcare Environments

SkinLesNet: Classification of Skin Lesions and Detection of Melanoma Cancer Using a Novel Multi-Layer Deep Convolutional Neural Network

Automatic Network Intrusion Detection System Using Machine learning and Deep learning

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