Classification of brain tumor types by deep learning with convolutional neural network on magnetic resonance images using a developed web-based interface

Ucuzal, Hasan; Yaşar, Şeyma; Çolak, Cemil

doi:10.1109/ismsit.2019.8932761

Cited by 33 publications

(3 citation statements)

References 9 publications

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“…Deep Learning has emerged as a remarkable tool for processing vast volumes of substantial data, surpassing numerous conventional methodologies. In reference [53], pioneers unveiled a groundbreaking web-based Deep Learning software, crafted in Python and harnessed within the Keras framework, meticulously tailored for the analysis of T1weighted contrast-enhanced images. This software initiates its operation by subjecting the input images to an array of preprocessing techniques, encompassing rotations, rescaling, and truncations.…”

Section: A Studies Published In 2019mentioning

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: A Studies Published In 2019mentioning

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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“…A good example is provided by Ucuzal et al, who developed web-based DL software aimed at the differential diagnosis of brain tumors using the popular Python programming language and the dedicated Keras library. Their software accepts multiple formats of the images, such as .jpeg, .jpg, and .png, and can be used to classify the input MRI image datasets into three diagnostic classes: meningioma, glioma, and pituitary tumors [ 60 ].…”

Section: Lesion Detection and Differential Diagnosismentioning

confidence: 99%

Artificial Intelligence in Brain Tumor Imaging: A Step toward Personalized Medicine

et al. 2023

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The application of artificial intelligence (AI) is accelerating the paradigm shift towards patient-tailored brain tumor management, achieving optimal onco-functional balance for each individual. AI-based models can positively impact different stages of the diagnostic and therapeutic process. Although the histological investigation will remain difficult to replace, in the near future the radiomic approach will allow a complementary, repeatable and non-invasive characterization of the lesion, assisting oncologists and neurosurgeons in selecting the best therapeutic option and the correct molecular target in chemotherapy. AI-driven tools are already playing an important role in surgical planning, delimiting the extent of the lesion (segmentation) and its relationships with the brain structures, thus allowing precision brain surgery as radical as reasonably acceptable to preserve the quality of life. Finally, AI-assisted models allow the prediction of complications, recurrences and therapeutic response, suggesting the most appropriate follow-up. Looking to the future, AI-powered models promise to integrate biochemical and clinical data to stratify risk and direct patients to personalized screening protocols.

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“…Ucuzal et al [ 106 ] developed a deep learning free web-based software that can be utilized in the detection and diagnosis of the three types of brain tumors (glioma/meningioma/pituitary) on T1-weighted magnetic resonance imaging. In the research, 3064 T1-weighted MR image scans for the three types of brain tumors have been used.…”

Section: Deep Learning In Tumor Detection Segmentation and Classificationmentioning

confidence: 99%

Deep Learning in Selected Cancers’ Image Analysis—A Survey

et al. 2020

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Deep learning algorithms have become the first choice as an approach to medical image analysis, face recognition, and emotion recognition. In this survey, several deep-learning-based approaches applied to breast cancer, cervical cancer, brain tumor, colon and lung cancers are studied and reviewed. Deep learning has been applied in almost all of the imaging modalities used for cervical and breast cancers and MRIs for the brain tumor. The result of the review process indicated that deep learning methods have achieved state-of-the-art in tumor detection, segmentation, feature extraction and classification. As presented in this paper, the deep learning approaches were used in three different modes that include training from scratch, transfer learning through freezing some layers of the deep learning network and modifying the architecture to reduce the number of parameters existing in the network. Moreover, the application of deep learning to imaging devices for the detection of various cancer cases has been studied by researchers affiliated to academic and medical institutes in economically developed countries; while, the study has not had much attention in Africa despite the dramatic soar of cancer risks in the continent.

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Classification of brain tumor types by deep learning with convolutional neural network on magnetic resonance images using a developed web-based interface

Cited by 33 publications

References 9 publications

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

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

Artificial Intelligence in Brain Tumor Imaging: A Step toward Personalized Medicine

Deep Learning in Selected Cancers’ Image Analysis—A Survey

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