Breast cancer diagnosis from histopathology images using deep neural network and XGBoost

Maleki, Alireza; Raahemi, Mohammad; Nasiri, Hamid

doi:10.1016/j.bspc.2023.105152

Cited by 38 publications

(8 citation statements)

References 23 publications

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“…This approach is called gradient boosting, as the subsequent models try to minimize the gradient of the loss function concerning the predicted values ( Ayyadevara & Ayyadevara, 2018 ; Chelgani et al, 2023 ). The learning objective function for XGBoost is a regularized version of the loss function, which helps prevent overfitting and improves generalization ( Liu et al, 2021 ; Maleki, Raahemi & Nasiri, 2023 ).…”

Section: Methodsmentioning

confidence: 99%

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Development of an expert system for the classification of myalgic encephalomyelitis/chronic fatigue syndrome

Yagin,

Shateri,

Nasiri

et al. 2024

PeerJ Computer Science

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Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a severe condition with an uncertain origin and a dismal prognosis. There is presently no precise diagnostic test for ME/CFS, and the diagnosis is determined primarily by the presence of certain symptoms. The current study presents an explainable artificial intelligence (XAI) integrated machine learning (ML) framework that identifies and classifies potential metabolic biomarkers of ME/CFS. Metabolomic data from blood samples from 19 controls and 32 ME/CFS patients, all female, who were between age and body mass index (BMI) frequency-matched groups, were used to develop the XAI-based model. The dataset contained 832 metabolites, and after feature selection, the model was developed using only 50 metabolites, meaning less medical knowledge is required, thus reducing diagnostic costs and improving prognostic time. The computational method was developed using six different ML algorithms before and after feature selection. The final classification model was explained using the XAI approach, SHAP. The best-performing classification model (XGBoost) achieved an area under the receiver operating characteristic curve (AUCROC) value of 98.85%. SHAP results showed that decreased levels of alpha-CEHC sulfate, hypoxanthine, and phenylacetylglutamine, as well as increased levels of N-delta-acetylornithine and oleoyl-linoloyl-glycerol (18:1/18:2)[2], increased the risk of ME/CFS. Besides the robustness of the methodology used, the results showed that the combination of ML and XAI could explain the biomarker prediction of ME/CFS and provided a first step toward establishing prognostic models for ME/CFS.

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Section: Methodsmentioning

confidence: 99%

“…where θ denotes the model parameters, n is the number of instances, l is the loss function that measures the difference between the predicted and true values, and Ω is the penalizing regularization function for complicated models ( Maleki, Raahemi & Nasiri, 2023 ) and is computed as: …”

Section: Methodsmentioning

confidence: 99%

Development of an expert system for the classification of myalgic encephalomyelitis/chronic fatigue syndrome

Yagin,

Shateri,

Nasiri

et al. 2024

PeerJ Computer Science

Self Cite

View full text Add to dashboard Cite

show abstract

“…The accuracy they showed in their proposed research work was 98.62%. The methodologies introduced by Maleki et al [37] aim to enhance the speed and precision of histopathological image classification, a critical challenge for therapeutic measures. Three different classifiers and six pre-trained networks are evaluated.…”

Section: Object Detection Modelsmentioning

confidence: 99%

“…A comparative summary of various author-recommended methods for breast cancer detection is presented in Table 5, including 'BreastCDNet,' along with the respective years of publication, applied techniques, datasets used, and achieved accuracy or performance metrics, thus showcasing the diversity in approaches and outcomes. 34 support vector machines 35 multilayer perceptrons 36 Wisconsin Diagnostic Breast Cancer 37 Area Under the Curve…”

Section: Performance Evaluationmentioning

confidence: 99%

“…Despite their efforts, their accuracy remains notably lower than the 99.14% accuracy reported in our model assessment. Maleki et al[37] employed transfer learning with XGBoost for histopathological image classification, reporting accuracies ranging from 89.1% to 93.8% at various magnifications on the BreakHis dataset. These accuracies are notably lower than the 99.14% achieved by our breast cancer detection model.…”

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confidence: 99%

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BreastCDNet: Breast Cancer Detection Neural Network, Classification and Localization

Mahichi,

Ghods,

Sohrabi

et al. 2023

Preprint

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Breast cancer remains a significant health concern globally, necessitating advanced detection methods for improved patient outcomes. Convolutional neural networks, CNN, have revolutionized object detection by automating the feature extraction process and allowing for the efficient localization and classification of objects within images. BreastCDNet introduces a novel approach to breast cancer detection using CNN. This pioneering CNN method is tailored specifically for the task of ultrasound breast cancer detection and localization, offering a streamlined and effective approach that significantly enhances diagnostic accuracy. Multi-task learning is leveraged by the proposed model, with simultaneous consideration of breast ultrasound image classification and bounding box regression for lesion localization. Intricate features from medical images are extracted by BreastCDNet architecture, facilitating both classification (benign or malignant) and object localization. Separate data generators are employed for classification and bounding box labels to optimize model training. The model's effectiveness is demonstrated by experimental results on the BUSI dataset, where exceptional classification accuracy and precise bounding box predictions are achieved. Key components, including convolutional layers for feature extraction, max-pooling layers for spatial reduction, fully connected layers for predictions, and specific output layers for classification and bounding box regression, are encompassed by the model architecture. The BreastCDNet model is a high-performance breast cancer detection model that achieved 99.14% training accuracy, 97.70% validation accuracy, 0.99 ROC AUC, and 0.97 F1-score on the training set. It is also capable of accurately localizing breast cancer lesions with an IOU score of 0.95.

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Breast histopathological imaging using ultra‐fast fluorescence confocal microscopy to identify cancer lesions at early stage

Mujahid,

Khan,

Kolivand

et al. 2024

Microscopy Res & Technique

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Ultrafast fluorescent confocal microscopy is a hypothetical approach for breast cancer detection because of its potential to achieve instantaneous, high‐resolution images of cellular‐level tissue features. Traditional approaches such as mammography and biopsy are laborious, invasive, and inefficient; confocal microscopy offers many benefits over these approaches. However, confocal microscopy enables the exact differentiation of malignant cells, the expeditious examination of extensive tissue sections, and the optical sectioning of tissue samples into tiny slices. The primary goal should be to prevent cancer altogether, although detecting it early can help achieve that objective. This research presents a novel Breast Histopathology Convolutional Neural Network (BHCNN) for feature extraction and recursive feature elimination method for selecting the most significant features. The proposed approach utilizes full slide images to identify tissue in regions affected by invasive ductal carcinoma. In addition, a transfer learning approach is employed to enhance the performance and accuracy of the models in detecting breast cancer, while also reducing computation time by modifying the final layer of the proposed model. The results showed that the BHCNN model outperformed other models in terms of accuracy, achieving a testing accuracy of 98.42% and a training accuracy of 99.94%. The confusion matrix results show that the IDC positive (+) class achieved 97.44% accuracy and 2.56% inaccurate results, while the IDC negative (−) class achieved 98.73% accuracy and 1.27% inaccurate results. Furthermore, the model achieved less than 0.05 validation loss.Research Highlights The objective is to develop an innovative framework using ultra‐fast fluorescence confocal microscopy, particularly for the challenging problem of breast cancer diagnosis. This framework will extract essential features from microscopy and employ a gradient recurrent unit for detection. The proposed research offers significant potential in enhancing medical imaging through the provision of a reliable and resilient system for precise diagnosis of breast cancer, thereby propelling the progression of state‐of‐the‐art medical technology. The most suitable feature was determined using BHRFE optimization techniques after retrieving the features by proposed model. Finally, the features chosen are integrated into a proposed methodology, which is then classified using a GRU deep model. The aforementioned research has significant potential to improve medical imaging by providing a complex and reliable system for precise evaluation of breast cancer, hence advancing the development of cutting‐edge medical technology.

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Breast cancer diagnosis from histopathology images using deep neural network and XGBoost

Cited by 38 publications

References 23 publications

Development of an expert system for the classification of myalgic encephalomyelitis/chronic fatigue syndrome

Development of an expert system for the classification of myalgic encephalomyelitis/chronic fatigue syndrome

BreastCDNet: Breast Cancer Detection Neural Network, Classification and Localization

Breast histopathological imaging using ultra‐fast fluorescence confocal microscopy to identify cancer lesions at early stage

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