Multi-class classification of brain tumor types from MR images using EfficientNets

Zulfiqar, Fatima; Bajwa, Usama Ijaz; Mehmood, Yasar

doi:10.1016/j.bspc.2023.104777

Cited by 72 publications

(28 citation statements)

References 31 publications

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“…EfficientNetB4 is a deep learning model of the EfficientNet family that is noted for its remarkable efficiency and performance ( Zulfiqar, Bajwa & Mehmood, 2023 ). These models were created by Google AI and are intended to maximize the balance between model size, accuracy, and computing efficiency.…”

Section: Proposed Approachmentioning

confidence: 99%

Transfer learning based approach for lung and colon cancer detection using local binary pattern features and explainable artificial intelligence (AI) techniques

Alsubai

2024

PeerJ Computer Science

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Cancer, a life-threatening disorder caused by genetic abnormalities and metabolic irregularities, is a substantial health danger, with lung and colon cancer being major contributors to death. Histopathological identification is critical in directing effective treatment regimens for these cancers. The earlier these disorders are identified, the lesser the risk of death. The use of machine learning and deep learning approaches has the potential to speed up cancer diagnosis processes by allowing researchers to analyse large patient databases quickly and affordably. This study introduces the Inception-ResNetV2 model with strategically incorporated local binary patterns (LBP) features to improve diagnostic accuracy for lung and colon cancer identification. The model is trained on histopathological images, and the integration of deep learning and texture-based features has demonstrated its exceptional performance with 99.98% accuracy. Importantly, the study employs explainable artificial intelligence (AI) through SHapley Additive exPlanations (SHAP) to unravel the complex inner workings of deep learning models, providing transparency in decision-making processes. This study highlights the potential to revolutionize cancer diagnosis in an era of more accurate and reliable medical assessments.

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Section: Proposed Approachmentioning

confidence: 99%

Transfer learning based approach for lung and colon cancer detection using local binary pattern features and explainable artificial intelligence (AI) techniques

Alsubai

2024

PeerJ Computer Science

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“…Using a data augmentation technique is crucial in addressing the abovementioned difficulty by expanding limited datasets. In a study conducted in reference [21], the authors employed PCA to assess the effects of several data augmentation techniques on the ResNet50 network in the context of brain tumour identification. The approach under consideration demonstrated a detection score of 92.34% in F1 after undergoing training from both a starting point and the ImageNet dataset.…”

Section: Brain Tumour Classification Using Machine Learning and Deep ...mentioning

confidence: 99%

MEHW‐SVM multi‐kernel approach for improved brain tumour classification

Dheepak,

Anita Christaline,

Vaishali

2023

IET Image Processing

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The human brain, the primary constituent of the nervous system, exhibits distinctive complexities that present considerable difficulties for healthcare practitioners, specifically in categorizing brain tumours. Magnetic resonance imaging is a widely favoured imaging modality for detecting brain tumours due to its extensive range of image characteristics and utilization of non‐ionizing radiation. The primary objective of the current investigation is to differentiate between three distinct classifications of brain tumours by introducing a novel methodology. The utilization of a combined feature extraction technique that integrates novel global grey level co‐occurrence matrix and local binary patterns is employed, thereby offering a comprehensive representation of the structural and textural information contained within the images. Principal component analysis is used to improve the model's efficiency for effective feature selection and dimensionality reduction. This study presents a novel framework incorporating four separate kernel functions, Minkowski–Gaussian, exponential support vector machine (SVM), histogram intersection SVM, and wavelet kernel, into a SVM classifier. The ensemble kernel employed in this study is specifically designed to classify glioma, meningioma, and pituitary tumours. Its implementation enhances the model's robustness and adaptability, surpassing the performance of conventional single‐kernel SVM approaches. This study substantially contributes to medical image classification by utilizing innovative kernel functions and advanced machine‐learning techniques. The findings demonstrate the potential for enhanced diagnostic accuracy in brain tumour cases. The presented approach shows promise in effectively addressing the intricate challenges associated with classifying brain tumours.

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“…Experimental results demonstrated that CNNs trained using transfer learning and fine-tuning were employed for glioma grading, achieving improved performance compared to traditional machine learning methods reliant on manual features, as well as compared to CNNs trained from scratch. Swati et al (2019) and Zulfiqar et al (2023) employed VGG19 and EfficientNetB2, respectively for the classification of brain tumors. Arora et al (2023) examined the classification performance of 14 pre-trained models for the identification of skin diseases.…”

Section: Introductionmentioning

confidence: 99%

A robust approach for multi-type classification of brain tumor using deep feature fusion

Chen,

Tan,

Zhang

et al. 2024

Front. Neurosci.

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Brain tumors can be classified into many different types based on their shape, texture, and location. Accurate diagnosis of brain tumor types can help doctors to develop appropriate treatment plans to save patients’ lives. Therefore, it is very crucial to improve the accuracy of this classification system for brain tumors to assist doctors in their treatment. We propose a deep feature fusion method based on convolutional neural networks to enhance the accuracy and robustness of brain tumor classification while mitigating the risk of over-fitting. Firstly, the extracted features of three pre-trained models including ResNet101, DenseNet121, and EfficientNetB0 are adjusted to ensure that the shape of extracted features for the three models is the same. Secondly, the three models are fine-tuned to extract features from brain tumor images. Thirdly, pairwise summation of the extracted features is carried out to achieve feature fusion. Finally, classification of brain tumors based on fused features is performed. The public datasets including Figshare (Dataset 1) and Kaggle (Dataset 2) are used to verify the reliability of the proposed method. Experimental results demonstrate that the fusion method of ResNet101 and DenseNet121 features achieves the best performance, which achieves classification accuracy of 99.18 and 97.24% in Figshare dataset and Kaggle dataset, respectively.

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Multi-class classification of brain tumor types from MR images using EfficientNets

Cited by 72 publications

References 31 publications

Transfer learning based approach for lung and colon cancer detection using local binary pattern features and explainable artificial intelligence (AI) techniques

Transfer learning based approach for lung and colon cancer detection using local binary pattern features and explainable artificial intelligence (AI) techniques

MEHW‐SVM multi‐kernel approach for improved brain tumour classification

A robust approach for multi-type classification of brain tumor using deep feature fusion

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