A Systematic Review of Zosteriform Rash in Breast Cancer Patients

Saud, Nouf Al; Jabbour, Samer; Kechichian, Elio; Aderian, Sandrine Sarine; Boccara, David; Nasr, Marwan; Hachem, Lena El; Mimoun, Maurice

doi:10.1097/sap.0000000000001505

Cited by 5 publications

(1 citation statement)

References 38 publications

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“…Despite extensive eforts from medical professionals and researchers, a defnitive method for treating breast cancer has yet to be established and reliable evidence for its prevention remains elusive [8][9][10][11]. Some components of breast cancer tissues are highly malignant and pose a severe danger to patients' lives as they can spread to other vital organs [12][13][14][15]. Te growth of mammary cells can lead to tumors in women.…”

Section: Introductionmentioning

confidence: 99%

Efficient Breast Cancer Diagnosis from Complex Mammographic Images Using Deep Convolutional Neural Network

Bukht

Ahmad²,

Almadhor

et al. 2023

Computational Intelligence and Neuroscience

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Medical image analysis places a significant focus on breast cancer, which poses a significant threat to women’s health and contributes to many fatalities. An early and precise diagnosis of breast cancer through digital mammograms can significantly improve the accuracy of disease detection. Computer-aided diagnosis (CAD) systems must analyze the medical imagery and perform detection, segmentation, and classification processes to assist radiologists with accurately detecting breast lesions. However, early-stage mammography cancer detection is certainly difficult. The deep convolutional neural network has demonstrated exceptional results and is considered a highly effective tool in the field. This study proposes a computational framework for diagnosing breast cancer using a ResNet-50 convolutional neural network to classify mammogram images. To train and classify the INbreast dataset into benign or malignant categories, the framework utilizes transfer learning from the pretrained ResNet-50 CNN on ImageNet. The results revealed that the proposed framework achieved an outstanding classification accuracy of 93%, surpassing other models trained on the same dataset. This novel approach facilitates early diagnosis and classification of malignant and benign breast cancer, potentially saving lives and resources. These outcomes highlight that deep convolutional neural network algorithms can be trained to achieve highly accurate results in various mammograms, along with the capacity to enhance medical tools by reducing the error rate in screening mammograms.

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