An Efficient Convolutional Neural Network Model for Brain MRI Segmentation

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dc.contributor.authorAbd El Kader, Isselmou
dc.contributor.authorXu, Guizhi
dc.contributor.authorZhang, Shuai
dc.contributor.authorBRAHIM, 2EL MAALOUMA SIDI
dc.contributor.authorSaminu, Sani
dc.date.accessioned2023-08-28T10:02:35Z
dc.date.available2023-08-28T10:02:35Z
dc.date.issued2022-05-05
dc.description.abstractMedical image analysis is a very interesting research area, and it is a significant challenge for researchers. Due to the complexity of the brain structure, accurate diagnosis of brain tumors is extremely difficult. In recent years, research focused on medical image processing to solve this problem by relying on deep learning techniques, and it has achieved good results in this field. This paper proposes an efficient convolutional neural network model for MR brain image segmentation and analysis. The novel model consists of segmentation efficient-CNN and pre-efficient-CNN blocks for dataset diminution and improvement blocks. The unique efficient-CNN is specially designed according to the model proposed by ASCNN (application) CNN-specific) to perform unidirectional and transverse feature extraction and tumor and pixel classification. The recommended Full-ReLU activation feature halves the number of cores in a high-coil filtered winding layer without reducing process quality. In this specific efficient-CNN consists of 8 convolutional layers and 110 kernels. The experiment results were done using the MR brain database from the Arizona university, including eluding with and without tumor images. The proposal model achieved an accuracy of 97.2% to 98%, which proves the efficiency of the model and its ability to assist in the early diagnosis of brain tumors with sufficient accuracy to support the doctors' decision during diagnosis.en_US
dc.description.abstractMedical image analysis is a very interesting research area, and it is a significant challenge for researchers. Due to the complexity of the brain structure, accurate diagnosis of brain tumors is extremely difficult. In recent years, research focused on medical image processing to solve this problem by relying on deep learning techniques, and it has achieved good results in this field. This paper proposes an efficient convolutional neural network model for MR brain image segmentation and analysis. The novel model consists of segmentation efficient-CNN and pre-efficient-CNN blocks for dataset diminution and improvement blocks. The unique efficient-CNN is specially designed according to the model proposed by ASCNN (application) CNN-specific) to perform unidirectional and transverse feature extraction and tumor and pixel classification. The recommended Full-ReLU activation feature halves the number of cores in a high-coil filtered winding layer without reducing process quality. In this specific efficient-CNN consists of 8 convolutional layers and 110 kernels. The experiment results were done using the MR brain database from the Arizona university, including eluding with and without tumor images. The proposal model achieved an accuracy of 97.2% to 98%, which proves the efficiency of the model and its ability to assist in the early diagnosis of brain tumors with sufficient accuracy to support the doctors' decision during diagnosis.en_US
dc.identifier.issn2224-2902
dc.identifier.urihttps://uilspace.unilorin.edu.ng/handle/20.500.12484/11699
dc.language.isoenen_US
dc.publisherWSEASen_US
dc.subjectBrain Tumor, MRI databases, Medical Image, CNN Model and accuracyen_US
dc.subjectBrain Tumor, MRI databases, Medical Image, CNN Model and accuracyen_US
dc.titleAn Efficient Convolutional Neural Network Model for Brain MRI Segmentationen_US
dc.typeArticleen_US

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