Automated brain tumor detection and classification using weighted fuzzy clustering algorithm, deep auto encoder with barnacle mating algorithm and random forest classifier techniques

Anantharajan, Shenbagarajan; Gunasekaran, S.

doi:10.1002/ima.22582

Cited by 6 publications

(2 citation statements)

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“…These findings underscore the LeUNet architecture's efficacy, particularly in anomaly detection within brain MR images, and signal its potential for advancing the field of medical image analysis. [80] A strategy for segmenting brain tumors in MRIs using a weighted fuzzy factor based on kernel metrics is proposed. To increase prediction accuracy, a deep autoencoder (DAE) is used in conjunction with the barnacle mating algorithm (BMOA) and random forest (RF) classifier.…”

Section: Studies Published In 2021mentioning

confidence: 99%

Brain Tumor Classification and Detection Based DL Models: A Systematic Review

Neamah,

Mohamed,

Adnan

et al. 2024

IEEE Access

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In recent years, the realms of computer vision and deep learning have ushered in transformative changes across various domains. Among these, deep learning stands out for its remarkable capacity to handle vast datasets, revolutionizing numerous fields, including the biomedical sector. In particular, its prowess has been harnessed in the realm of brain tumor identification through MRI scans, yielding impressive results. This research project is dedicated to conducting an exhaustive exploration of existing endeavors in the domain of brain tumor identification and classification via MRI scans. This endeavor is poised to be of profound value to researchers looking to leverage their deep learning expertise in the realm of brain tumor detection and categorization. The initial phase involves an overview of prior studies that have employed deep learning for categorising and detecting brain tumors. Subsequently, a meticulous analysis of deep learning studies proposed in research publications spanning (2019 to 2022) is presented in tabular form. The conclusion section comprehensively assesses the merits and demerits inherent in deep neural networks. The insights gleaned from this study promise to equip future researchers with a holistic perspective on current research trends and a nuanced understanding of the effectiveness of diverse deep learning methodologies. It is our fervent belief that this research will significantly advance the understanding of brain tumors and their detection methodologies.

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Section: Studies Published In 2021mentioning

confidence: 99%

Brain Tumor Classification and Detection Based DL Models: A Systematic Review

Neamah,

Mohamed,

Adnan

et al. 2024

IEEE Access

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“…In 2021, Anantharajan and Gunasekaran [25] used a weighted fuzzy factor approach based on kernel metrics. To improve prediction accuracy, a deep autoencoder (DAE) combined with a weighted fuzzy clustering algorithm was applied in order to provide a segmentation for the lesion area from the remaining parts of MRI image.…”

Section: Related Workmentioning

confidence: 99%

Automated brain tumor detection of MRI image based on hybrid image processing techniques

2022

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Primary challenges are the identification, segmentation, and extraction of the afflicted area from the scanning of magnetic resonance. However, it is a time-consuming and tiresome for clinical specialists. In this paper, an automated brain tumor system is proposed. The proposed system employs hybrid image processing techniques such as contrast correction, histogram normalization, thresholding techniques, arithmetic, and morphological operations to quarantine nearby organs and other tissue from the brain for improving the localization of the affected region. At first, the skull stripping process is proposed to segregate the non-designated regions to extract the designated brain regions. Those resultant brain region images are further subjected to discover the brain tumor. The planned scheme is studied on the magnetic resonance (MR) images with the use of T1, T2, T1c, and fluid-attenuated inversion recovery (FLAIR). The proposed hybrid method employed. The results reveal that the proposed method is quite efficient to extract the tumor region. The accuracy rate for segmentation and separation of area of interest in brain tumor reached to 95%. Finally, the significance of the proposed procedure is confirmed using the real image clinical dataset got from ten patients were diagnosed as begin, malignant, and metastatic brain tumors in Al-Yarmouk and Baghdad teaching hospital in Baghdad, Iraq.

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