Automation of Brain Tumor Identification using EfficientNet on Magnetic Resonance Images

Tripathy, Sushreeta; Singh, Rishabh; Ray, Mousim

doi:10.1016/j.procs.2023.01.133

Cited by 24 publications

(4 citation statements)

References 12 publications

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“…With this approach, EfficientNet achieved great accuracies on classical datasets such as ImageNet while being 8.4× smaller and 6.1× faster on inference than the previous convolutional neural networks. This EfficientNet architecture has shown great performance in some recent studies about brain tumor ( Tripathy, Singh & Ray, 2023 ; Nayak et al, 2022 ). Some EfficientNet models were evaluated but only results of the best one, EfficientNetB4, were shown in this article.…”

Section: Methodsmentioning

confidence: 99%

Survival and grade of the glioma prediction using transfer learning

Valbuena Rubio,

García-Ordás,

García-Olalla Olivera

et al. 2023

PeerJ Computer Science

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Glioblastoma is a highly malignant brain tumor with a life expectancy of only 3–6 months without treatment. Detecting and predicting its survival and grade accurately are crucial. This study introduces a novel approach using transfer learning techniques. Various pre-trained networks, including EfficientNet, ResNet, VGG16, and Inception, were tested through exhaustive optimization to identify the most suitable architecture. Transfer learning was applied to fine-tune these models on a glioblastoma image dataset, aiming to achieve two objectives: survival and tumor grade prediction.The experimental results show 65% accuracy in survival prediction, classifying patients into short, medium, or long survival categories. Additionally, the prediction of tumor grade achieved an accuracy of 97%, accurately differentiating low-grade gliomas (LGG) and high-grade gliomas (HGG). The success of the approach is attributed to the effectiveness of transfer learning, surpassing the current state-of-the-art methods. In conclusion, this study presents a promising method for predicting the survival and grade of glioblastoma. Transfer learning demonstrates its potential in enhancing prediction models, particularly in scenarios with limited large datasets. These findings hold promise for improving diagnostic and treatment approaches for glioblastoma patients.

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Section: Methodsmentioning

confidence: 99%

Survival and grade of the glioma prediction using transfer learning

Valbuena Rubio,

García-Ordás,

García-Olalla Olivera

et al. 2023

PeerJ Computer Science

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“…To enhance accuracy, many deep learning architectures attempt to add a growing number of layers, which reduces processing power and causes gradient descent difficulties [12]- [14]. EfficientNet, in contrast to other architectural innovations, employs a scalable and balanced increase of layer thickness and width [10], [15].…”

Section: B Efficientnetmentioning

confidence: 99%

Brain Tumor Classification Using Four Versions of EfficientNet

Hastomo,

Karno,

Arif

et al. 2023

Insearch

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Medical image processing approaches for detecting brain cancers are still primarily done manually, with low accuracy and taking a long period. Furthermore, this task can only be done by professionals with a high degree of medical competence, and the number of experts is obviously restricted in comparison to the large number of patients who need to be treated. With the growth of artificial intelligence and the rapid development of computers in terms of processing speed and storage capacity, it is feasible to assist doctors in classifying the existence of tumors in the head. This study employs four variations of the EfficientNet architecture to train a model on a variety of MRI imaging data. The model version B1 was shown to be the best in this investigation, with 98% accuracy, 99% precision, 95% recall, and 97% f1 score from versions B0 to B3 (4 versions). These results are excellent, but they do not rule out additional study utilizing various forms of design.

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“…Research related to brain tumors using deep learning methods has attracted the interest of world researchers (Dang et al, 2022;Demir et al, 2023;Emam et al, 2023;Farajzadeh et al, 2023;Kanchanamala et al, 2023;Mehnatkesh et al, 2023;Tabatabaei et al, 2023). Previous research used the Convolutional Neural Networks (CNN) method with the EfficientNet architecture (Isunuri & Kakarla, 2023;Nayak et al, 2022;Shah et al, 2022;Tripathy et al, 2023;Zulfiqar et al, 2023), multi-class Support Vector Machine (SVM) and fuzzy classifier (Vankdothu & Hameed, 2022), hybrid model combined CNN and SVM (Khairandish et al, 2022), SVM and Artificial Neural Network (ANN) (Sachdeva et al, 2016), hybrid machine learning (ML) k-nearest https://journal.umy.ac.id/index.php/st/issue/view/1064 neighbour and k-means clustering (Rinesh et al, 2022), accelerated particle swarm optimization (APSO) based artificial neural network model (ANNM) (Pradeep et al, 2022), particle swarm optimization (PCA) algorithms (Zahid et al, 2022), atomic force microscopy (AFM) (Huml et al, 2023), CNN-pretrained ResNet-50, Inception-v3, and VGG-16 (Srinivas et al, 2022), Genetic Algorithm and U-Net (Arif et al, 2022).…”

Section: Introductionmentioning

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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Automation of Brain Tumor Identification using EfficientNet on Magnetic Resonance Images

Cited by 24 publications

References 12 publications

Survival and grade of the glioma prediction using transfer learning

Survival and grade of the glioma prediction using transfer learning

Brain Tumor Classification Using Four Versions of EfficientNet

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

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