Detection and 3D Visualization of Brain Tumor using Deep Learning and Polynomial Interpolation

Tuhin, Md. Akram Hossan; Pramanick, Tarunya; Emon, Humayoun Kabir; Rahman, Wahida; Rahi, Md. Muzahidul Islam; Alam, Md. Ashraful

doi:10.1109/csde50874.2020.9411595

Cited by 4 publications

(2 citation statements)

References 26 publications

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“…Results consistently indicate promising classification accuracies, with some studies reporting mean accuracies of up to 98%, outperforming traditional methods. Advancements in technology have led to the utilization of 3D scanning for tumor analysis, as discussed in [6]. The study explores 3D image processing methods for brain tumor detection and classification, employing deep learning frameworks like MobileNetV2, MobileNetV3 (both small and large variants), VGG16, VGG19, and custom CNN models.…”

Section: Related Workmentioning

confidence: 99%

An Effective Classification of Brain Tumor using Deep Learning Techniques

Raj

2024

IJRASET

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Brain tumor classification is a critical task in medical imaging, aiding in timely diagnosis and treatment planning. In this paper, we propose a comprehensive approach utilizing deep learning models for the classification of brain tumors into four categories: glioma, meningioma, pituitary tumors, and no tumors. We employ state-of-the-art convolutional neural network (CNN) architectures including ResNet50, DenseNet201, EfficientNetB3, and InceptionV3 for the classification task. To enhance the performance of the model, we employ resizing and augmentation techniques such as flips and rotation, thereby increasing the diversity of the training dataset. This is particularly crucial due to the limitations posed by small-sized datasets in previous methodologies. Our findings underscore the efficacy of deep learning approaches in brain tumor classification, with EfficientNetB3 emerging as a promising model for accurate diagnosis. Furthermore, our utilization of resizing and augmentation techniques demonstrates their significance in mitigating the challenges associated with limited training data.

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Section: Related Workmentioning

confidence: 99%

An Effective Classification of Brain Tumor using Deep Learning Techniques

Raj

2024

IJRASET

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“…With the recent developments in technology, 3D scanning is also being used for the analysis of tumors. 3D image processing for brain tumor detection and classification has been described in [48]. It used various deep learning frameworks, such as MobileNetV2, MobileNetV3 small, MobileNetV3 big, VGG16, VGG19, and custom CNN models.…”

Section: A Deep Learning Techniquesmentioning

confidence: 99%

Brain Tumor Detection and Classification Using Intelligence Techniques: An Overview

Solanki¹,

Singh

Chouhan

et al. 2023

IEEE Access

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A tumor is carried on by rapid and uncontrolled cell growth in the brain. If it is not treated in the initial phases, it could prove fatal. Despite numerous significant efforts and encouraging outcomes, accurate segmentation and classification continue to be a challenge. Detection of brain tumors is significantly complicated by the distinctions in tumor position, structure, and proportions. The main disinterest of this study stays to offer investigators, comprehensive literature on Magnetic Resonance (MR) imaging's ability to identify brain tumors. Using computational intelligence and statistical image processing techniques, this research paper proposed several ways to detect brain cancer and tumors. This study also shows an assessment matrix for a specific system using particular systems and dataset types. This paper also explains the morphology of brain tumors, accessible data sets, augmentation methods, component extraction, and categorization among Deep Learning (DL), Transfer Learning (TL), and Machine Learning (ML) models. Finally, our study compiles all relevant material for the identification of understanding tumors, including their benefits, drawbacks, advancements, and upcoming trends.

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