Multimodal breast cancer hybrid explainable computer-aided diagnosis using medical mammograms and ultrasound Images

• Autorzy: Al-Tam Riyadh M. , Al-Hejri Aymen M. , Alshamrani Sultan S. , Al-antari Mugahed A. , Narangale Sachin M.

Identyfikatory

DOI

10.1016/j.bbe.2024.08.007

• Języki publikacji: EN

Abstrakty

EN

Breast cancer is a prevalent global disease where early detection is crucial for effective treatment and reducing mortality rates. To address this challenge, a novel Computer-Aided Diagnosis (CAD) framework leveraging Artificial Intelligence (AI) techniques has been developed. This framework integrates capabilities for the simultaneous detection and classification of breast lesions. The AI-based CAD framework is meticulously structured into two pipelines (Stage 1 and Stage 2). The first pipeline (Stage 1) focuses on detectable cases where lesions are identified during the detection task. The second pipeline (Stage 2) is dedicated to cases where lesions are not initially detected. Various experimental scenarios, including binary (benign vs. malignant) and multiclass classifications based on BI-RADS scores, were conducted for training and evaluation. Additionally, a verification and validation (V&V) scenario was implemented to assess the reliability of the framework using unseen multimodal datasets for both binary and multi-class tasks. For the detection tasks, the recent AI detectors like YOLO (You Only Look Once) variants were fine-tuned and optimized to localize breast lesions. For classification tasks, hybrid AI models incorporating ensemble convolutional neural networks (CNNs) and the attention mechanism of Vision Transformers were proposed to enhance prediction performance. The proposed AI-based CAD framework was trained and evaluated using various multimodal ultrasound datasets (BUSI and US2) and mammogram datasets (MIAS, INbreast, real private mammograms, KAU-BCMD, and CBIS-DDSM), either individually or in merged forms. Visual t-SNE techniques were applied to visually harmonize data distributions across ultrasound and mammogram datasets for effective various datasets merging. To generate visually explainable heatmaps in both pipelines (stages 1 and 2), Grad-CAM was utilized. These heatmaps assisted in finalizing detected boxes, especially in stage 2 when the AI detector failed to automatically detect breast lesions. The highest evaluation metrics achieved for merged dataset (BUSI, INbreast, and MIAS) were 97.73% accuracy and 97.27% mAP50 in the first pipeline. In the second pipeline, the proposed CAD achieved 91.66% accuracy with 95.65% mAP50 on MIAS and 95.65% accuracy with 96.10% mAP50 on the merged dataset (INbreast and MIAS). Meanwhile, exceptional performance was demonstrated using BI-RADS scores, achieving 87.29% accuracy, 91.68% AUC, 86.72% mAP50, and 64.75% mAP50-95 on a combined dataset of INbreast and CBIS-DDSM. These results underscore the practical significance of the proposed CAD framework in automatically annotating suspected lesions for radiologists.

Słowa kluczowe

EN

breast cancer detection; breast cancer classification; BI-RAD score; ensemble transfer learning; hybrid CAD system; explainable heatmap

PL

wykrywanie raka piersi; klasyfikacja raka piersi; wynik Bi-RAD; system CAD

• Wydawca: Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences ; Elsevier
• Czasopismo: Biocybernetics and Biomedical Engineering
• Rocznik: 2024
• Tom: Vol. 44, no. 3
• Strony: 731--758
• Opis fizyczny: Bibliogr. 106 poz., rys., tab., wykr.

Twórcy

autor

Al-Tam Riyadh M.

• School of Computational Sciences, Swami Ramanand Teerth Marathwada University, Nanded 431606, Maharashtra, India
• Faculty of Administrative and Computer Sciences, University of Albaydha, Albaydha, Yemen

• https://orcid.org/0000-0002-9954-7529

autor

Al-Hejri Aymen M.

• School of Computational Sciences, Swami Ramanand Teerth Marathwada University, Nanded 431606, Maharashtra, India
• Faculty of Administrative and Computer Sciences, University of Albaydha, Albaydha, Yemen

• https://orcid.org/0000-0002-3405-5363

autor

Alshamrani Sultan S.

• Department of Information Technology, College of Computers and Information Technology, Taif University, PO Box 11099, 21944, Taif, Saudi Arabia

• https://orcid.org/0000-0001-8194-9354

autor

Al-antari Mugahed A.

• Department of Artificial Intelligence and Data Science, College of AI Convergence, Daeyang AI Center, Sejong University, Seoul 05006, Republic of Korea

• https://orcid.org/0000-0002-4457-4407

autor

Narangale Sachin M.

• School of Media Studies, Swami Ramanand Teerth Marathwada University, Nanded 431606, Maharashtra, India

• https://orcid.org/0000-0001-9023-5927

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr POPUL/SP/0154/2024/02 w ramach programu "Społeczna odpowiedzialność nauki II" - moduł: Popularyzacja nauki (2025).