Multi-Class Classification of Breast Cancer Using 6B-Net with Deep Feature Fusion and Selection Method

Umer, Muhammad Junaid; Sharif, Muhammad; Kadry, Seifedine; Alharbi, Abdullah

doi:10.3390/jpm12050683

Cited by 23 publications

(8 citation statements)

References 53 publications

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“…Umer et al 21 presented a DL method for multi-class BC classification using a 6B-Net deep CNN model enhanced with feature fusion and selection. Evaluated on BreaKHis with 7909 images across eight classes and another BC histopathology dataset of 3771 images in four classes, the approach achieved 94.20% accuracy for the four-class set in 226 s and 90.10% for the eight-class set in 147 s.…”

Section: Related Workmentioning

confidence: 99%

Fine tuning deep learning models for breast tumor classification

Heikal,

El-Ghamry,

Elmougy

et al. 2024

Sci Rep

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This paper proposes an approach to enhance the differentiation task between benign and malignant Breast Tumors (BT) using histopathology images from the BreakHis dataset. The main stages involve preprocessing, which encompasses image resizing, data partitioning (training and testing sets), followed by data augmentation techniques. Both feature extraction and classification tasks are employed by a Custom CNN. The experimental results show that the proposed approach using the Custom CNN model exhibits better performance with an accuracy of 84% than applying the same approach using other pretrained models, including MobileNetV3, EfficientNetB0, Vgg16, and ResNet50V2, that present relatively lower accuracies, ranging from 74 to 82%; these four models are used as both feature extractors and classifiers. To increase the accuracy and other performance metrics, Grey Wolf Optimization (GWO), and Modified Gorilla Troops Optimization (MGTO) metaheuristic optimizers are applied to each model separately for hyperparameter tuning. In this case, the experimental results show that the Custom CNN model, refined with MGTO optimization, reaches an exceptional accuracy of 93.13% in just 10 iterations, outperforming the other state-of-the-art methods, and the other four used pretrained models based on the BreakHis dataset.

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Section: Related Workmentioning

confidence: 99%

Fine tuning deep learning models for breast tumor classification

Heikal,

El-Ghamry,

Elmougy

et al. 2024

Sci Rep

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“…However, due to the complexity of early breast cancer and the dimming of mammography images, it is challenging for clinicians to diagnose cancer from these images. As a result, it is crucial to improve a doctor’s detection effectiveness using the CAD system of deep learning approaches [ 26 ].…”

Section: Related Workmentioning

confidence: 99%

BC2NetRF: Breast Cancer Classification from Mammogram Images Using Enhanced Deep Learning Features and Equilibrium-Jaya Controlled Regula Falsi-Based Features Selection

et al. 2023

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One of the most frequent cancers in women is breast cancer, and in the year 2022, approximately 287,850 new cases have been diagnosed. From them, 43,250 women died from this cancer. An early diagnosis of this cancer can help to overcome the mortality rate. However, the manual diagnosis of this cancer using mammogram images is not an easy process and always requires an expert person. Several AI-based techniques have been suggested in the literature. However, still, they are facing several challenges, such as similarities between cancer and non-cancer regions, irrelevant feature extraction, and weak training models. In this work, we proposed a new automated computerized framework for breast cancer classification. The proposed framework improves the contrast using a novel enhancement technique called haze-reduced local-global. The enhanced images are later employed for the dataset augmentation. This step aimed at increasing the diversity of the dataset and improving the training capability of the selected deep learning model. After that, a pre-trained model named EfficientNet-b0 was employed and fine-tuned to add a few new layers. The fine-tuned model was trained separately on original and enhanced images using deep transfer learning concepts with static hyperparameters’ initialization. Deep features were extracted from the average pooling layer in the next step and fused using a new serial-based approach. The fused features were later optimized using a feature selection algorithm known as Equilibrium-Jaya controlled Regula Falsi. The Regula Falsi was employed as a termination function in this algorithm. The selected features were finally classified using several machine learning classifiers. The experimental process was conducted on two publicly available datasets—CBIS-DDSM and INbreast. For these datasets, the achieved average accuracy is 95.4% and 99.7%. A comparison with state-of-the-art (SOTA) technology shows that the obtained proposed framework improved the accuracy. Moreover, the confidence interval-based analysis shows consistent results of the proposed framework.

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“…Two pre-trained models resnet34 and resnet50 were utilized to transfer the learned feature for classification tasks with different training testing ratios of the dataset [34]. For more understanding of BC classification and segmentation interested readers are referred to the latest studies found in [35][36][37][38].…”

Section: Related Workmentioning

confidence: 99%

A Framework of Deep Learning and Selection-Based Breast Cancer Detection from Histopathology Images

Umer¹,

Sharif²,

Alhaisoni³

et al. 2023

Computer Systems Science and Engineering

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Breast cancer (BC) is a most spreading and deadly cancerous malady which is mostly diagnosed in middle-aged women worldwide and effecting beyond a half-million people every year. The BC positive newly diagnosed cases in 2018 reached 2.1 million around the world with a death rate of 11.6% of total cases. Early diagnosis and detection of breast cancer disease with proper treatment may reduce the number of deaths. The gold standard for BC detection is biopsy analysis which needs an expert for correct diagnosis. Manual diagnosis of BC is a complex and challenging task. This work proposed a deep learning-based (DL) solution for the early detection of this deadly disease from histopathology images. To evaluate the robustness of the proposed method a large publically available breast histopathology image database containing a total of 277524 histopathology images is utilized. The proposed automatic diagnosis of BC detection and classification mainly involves three steps. Initially, a DL model is proposed for feature extraction. Secondly, the extracted feature vector (FV) is passed to the proposed novel feature selection (FS) framework for the best FS. Finally, for the classification of BC into invasive ductal carcinoma (IDC) and normal class different machine learning (ML) algorithms are used. Experimental outcomes of the proposed methodology achieved the highest accuracy of 92.7% which shows that the proposed technique can successfully be implemented for BC detection to aid the pathologists in the early and accurate diagnosis of BC.

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Multi-Class Classification of Breast Cancer Using 6B-Net with Deep Feature Fusion and Selection Method

Cited by 23 publications

References 53 publications

Fine tuning deep learning models for breast tumor classification

Fine tuning deep learning models for breast tumor classification

BC2NetRF: Breast Cancer Classification from Mammogram Images Using Enhanced Deep Learning Features and Equilibrium-Jaya Controlled Regula Falsi-Based Features Selection

A Framework of Deep Learning and Selection-Based Breast Cancer Detection from Histopathology Images

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