Brain Tumor Classification using EfficientNet-B0 Model

Goutham, Veeranki; Sameerunnisa, Abdul; Babu, Sallagundla; Prakash, Tugu Bhanu

doi:10.1109/icacite53722.2022.9823526

Cited by 9 publications

(2 citation statements)

References 15 publications

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“…Accuracy reaches 96.94% with the EfficientNet-B0 model. The Magnetic Resonance Images (MRI) have been loaded into EfficientNet, which continuously adds hidden layers to increase efficiency (Goutham et al, 2022). Research that has been conducted by (Lakshmi Veeranki et al, 2023) uses the EfficientNetB0, ResNet50, Xception, MobileNetV2, and VGG16 models, using Transfer Learning with the best results using the EfficientNet-B0 model of 97.61%.…”

Section: Resultsmentioning

confidence: 99%

Classification of Brain Image Tumor using EfficientNet B1-B2 Deep Learning

Hastomo,

Karno,

Sestri

et al. 2024

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In this study, a new neural network model (EfficientNet B1-B2) was sought for the detection of brain tumors in magnetic resonance imaging (MRI) images. The primary objective was to achieve high accuracy rates so as to classify the images. The deep learning techniques meticulously processed and increased the data augmentation as much as possible for the EfficientNet B1-B2 models. Our experimental results show an accuracy of 98% in the B1 version in Table II. This provides a potentially optimistic view of the application of artificial intelligence technology to disease diagnosis based on medical image analysis. Nonetheless, we must remind ourselves that the dataset we used has limitations in terms of the challenges it can pose. Although the number of potential variations of actual medical images constitutes a major challenge, it is not the only one. Most medical datasets are unbalanced, contain highly variable noise, have a slow internal structure, and are often small in size. Hence, our end goal is to help stimulate not only the field of brain tumor detection and treatment but also the development of more sophisticated classification models in the health context.

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Section: Resultsmentioning

confidence: 99%

Classification of Brain Image Tumor using EfficientNet B1-B2 Deep Learning

Hastomo,

Karno,

Sestri

et al. 2024

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“…ResNet50 and EfficientNetB0 have shown to provide good accuracy in [34] and [35] , respectively. In the first work, the authors obtained the best results without data augmentation, which aligns with our results.…”

Section: Related Workmentioning

confidence: 99%