Evaluation of the Role of Data Increment of Cancer Cases with Computer-Aided Algorithms for Detection of Breast Cancer
Imran Majeed Khan, H Rafique, Abdul Waheed Anwar, Basit Attique, Muhammad Jahanzab · Liaquat National Journal of Cancer Care · 2024
Background: Cancer is one of the leading causes of death and morbidity all over the world, with 14.1 million new cases and 8.2 million deaths due to cancer.Objective: Early breast cancer detection is important for the treatment and survival of patients.CAD is a useful tool for earlier cancer detection.Methods: There are 1863 malignant and benign cases.The nine features are extracted from the DDMS database, and the values are assigned using the BI-RAD mammography lexicon.The research is conducted at Radiation Oncology, AIMC/Jinnah Hospital, Lahore from October 2021 to November 2023.Mammography is an important medical imaging modality used for early diagnosis and detection of breast diseases.The data size plays an important role in applying artificial intelligence to cancer diagnosis.CBR stands for Case-based Reasoning is an established research in the Artificial Intelligence field.The CBR was used at multiple data increments to research its impact on the detection of breast cancer.Principal component Analysis (PCA) is applied to evaluate important features in mammograms to improve the precision and recall for the detection of breast cancer.Results: The recall of malignant test cases lies in the range of 0.78 to 0.88.The precision and recall for benign test cases vary between 0.82 to 0.89 and 0.85 to 1 respectively. Conclusion:Finally, the implementation of PCA on data results showed that the precision of malignant test cases increased, and recall decreased.The data increment proves to increase the detection of breast cancer.