Development of a Secure Cloud-based Breast Cancer Diagnosis System
Fazal-e- Amin, Muhammad Hussain, Zulfiqar Ali, Mariam Busaleh, Sarah A. Al Sultan · 2022
Breast cancer is one of the most common cancers that cause death in women. Breast cancer was the most common cancer in 2020, with 2.26 million new cases. Cancer mortalities can be reduced by early detection and treatment. There are two components of early detection: early diagnosis and screening. Our work is related to early diagnosis. Early diagnosis helps to start treatment early, which is more effective and less expensive. The lives of cancer patients can be improved significantly by early detection and avoiding treatment delays. This paper presents the design and development of a secure cloud-based breast cancer diagnosis system. The proposed system uses using convolutional neural network for breast mass classification. Fragile zero watermarking will be implemented to secure the transmission of mammographs. The proposed system will enable women living in remote areas to get the facility for the routine initial screening near their homes without making their identity and medical data vulnerable. It will reduce the cost and time by avoiding frequent visits of the patients to specialized health care centers. On the other hand, it will help enhance the quality of service at specialized healthcare centers by reducing the number of patients in the initial screening.