A comparative study of Detecting Covid 19 by Using Chest X-ray Images– A Deep Learning Approach

Miah, Jonayet; Khan, Razib Hayat; Ahmed, Shamsuddin; Mahmud, Md Ishtyaq

doi:10.1109/aiiot58121.2023.10174382

Cited by 11 publications

(1 citation statement)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Breast cancer poses a significant health risk affecting both women and men, making it one of the most prevalent and lifethreatening forms of cancer. The critical role of histopathological images in the detection and treatment of breast cancer, providing essential phenotypic information, has prompted the widespread utilization of Deep Neural Networks (DNNs) [1,2]. This study delves into a comprehensive analysis of pre-trained deep transfer learning models, specifically ResNet50, ResNet101, VGG16, and VGG19, Wolberg et al (1990) This paper utilizes breast cytology diagnosis as a case study to showcase the application of the method in medical diagnosis and decision-making.…”

Section: Introductionmentioning

confidence: 99%

Transforming Breast Cancer Identification: An In-Depth Examination of Advanced Machine Learning Models Applied to Histopathological Images

Rejon Kumar Ray,

Ahmed Ali Linkon,

Mohammad Shafiquzzaman Bhuiyan

et al. 2024

JCSTS

View full text Add to dashboard Cite

Breast cancer stands as one of the most prevalent and perilous forms of cancer affecting both women and men. The detection and treatment of breast cancer benefit significantly from histopathological images, which carry crucial phenotypic information. To enhance accuracy in breast cancer detection, Deep Neural Networks (DNNs) are commonly utilized. Our research delves into the analysis of pre-trained deep transfer learning models, including ResNet50, ResNet101, VGG16, and VGG19, for identifying breast cancer using a dataset comprising 2453 histopathology images. The dataset categorizes images into two groups: those featuring invasive ductal carcinoma (IDC) and those without IDC. Through our analysis of transfer learning models, we observed that ResNet50 outperformed the other models, achieving impressive metrics such as accuracy rates of 92.2%, Area under Curve (AUC) rates of 91.0%, recall rates of 95.7%, and a minimal loss of 3.5%.

show abstract

Section: Introductionmentioning

confidence: 99%