Harnessing the power of radiomics and deep learning for improved breast cancer diagnosis with multiparametric breast mammography

Mahmood, Tariq; Saba, Tanzila; Rehman, Amjad; Alamri, Faten S.

doi:10.1016/j.eswa.2024.123747

Cited by 2 publications

(1 citation statement)

References 45 publications

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“…Deep learning has made substantial advancements in cancer detection, particularly in the areas of lesion detection and segmentation. Many studies have shown that employing deep learning models can significantly enhance the accuracy of cancer detection [20][21][22], enabling effective differentiation between normal and cancerous tissue. Tan et al [23] proposed a small target breast mass detection network, introducing an adaptive positive sample selection algorithm to automatically select positive samples.…”

Section: Related Workmentioning

confidence: 99%

Colorectal Polyp Detection Model by Using Super-Resolution Reconstruction and YOLO

Wang,

Xie,

Cui

et al. 2024

Electronics

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Colorectal cancer (CRC) is the second leading cause of cancer-related deaths worldwide. Colonoscopy is the primary method to prevent CRC. However, traditional polyp detection methods face problems such as low image resolution and the possibility of missing polyps. In recent years, deep learning techniques have been extensively employed in the detection of colorectal polyps. However, these algorithms have not yet addressed the issue of detection in low-resolution images. In this study, we propose a novel YOLO-SRPD model by integrating SRGAN and YOLO to address the issue of low-resolution colonoscopy images. Firstly, the SRGAN with integrated ACmix is used to convert low-resolution images to high-resolution images. The generated high-resolution images are then used as the training set for polyp detection. Then, the C3_Res2Net is integrated into the YOLOv5 backbone to enhance multiscale feature extraction. Finally, CBAM modules are added before the prediction head to enhance attention to polyp information. The experimental results indicate that YOLO-SRPD achieves a mean average precision (mAP) of 94.2% and a precision of 95.2%. Compared to the original model (YOLOv5), the average accuracy increased by 1.8% and the recall rate increased by 5.6%. These experimental results confirm that YOLO-SRPD can address the low-resolution problem during colorectal polyp detection and exhibit exceptional robustness.

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Section: Related Workmentioning

confidence: 99%

Colorectal Polyp Detection Model by Using Super-Resolution Reconstruction and YOLO

Wang,

Xie,

Cui

et al. 2024

Electronics

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DM–AHR: A Self-Supervised Conditional Diffusion Model for AI-Generated Hairless Imaging for Enhanced Skin Diagnosis Applications

Benjdira,

M. Ali,

Koubaa

et al. 2024

Cancers

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Accurate skin diagnosis through end-user applications is important for early detection and cure of severe skin diseases. However, the low quality of dermoscopic images hampers this mission, especially with the presence of hair on these kinds of images. This paper introduces DM–AHR, a novel, self-supervised conditional diffusion model designed specifically for the automatic generation of hairless dermoscopic images to improve the quality of skin diagnosis applications. The current research contributes in three significant ways to the field of dermatologic imaging. First, we develop a customized diffusion model that adeptly differentiates between hair and skin features. Second, we pioneer a novel self-supervised learning strategy that is specifically tailored to optimize performance for hairless imaging. Third, we introduce a new dataset, named DERMAHAIR (DERMatologic Automatic HAIR Removal Dataset), that is designed to advance and benchmark research in this specialized domain. These contributions significantly enhance the clarity of dermoscopic images, improving the accuracy of skin diagnosis procedures. We elaborate on the architecture of DM–AHR and demonstrate its effective performance in removing hair while preserving critical details of skin lesions. Our results show an enhancement in the accuracy of skin lesion analysis when compared to existing techniques. Given its robust performance, DM–AHR holds considerable promise for broader application in medical image enhancement.

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Harnessing the power of radiomics and deep learning for improved breast cancer diagnosis with multiparametric breast mammography

Cited by 2 publications

References 45 publications

Colorectal Polyp Detection Model by Using Super-Resolution Reconstruction and YOLO

Colorectal Polyp Detection Model by Using Super-Resolution Reconstruction and YOLO

DM–AHR: A Self-Supervised Conditional Diffusion Model for AI-Generated Hairless Imaging for Enhanced Skin Diagnosis Applications

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