Abstract
The advancement of computer‐ and internet‐based technologies has transformed the nature of services in healthcare by using mobile devices in conjunction with cloud computing. The classical phenomenon of patient–doctor diagnostics is extended to a more robust advanced concept of E‐health, where remote online/offline treatment and diagnostics can be performed. In this article, we propose a framework which incorporates a cloud‐based decision support system for the detection and classification of malignant cells in breast cancer, while using breast cytology images. In the proposed approach, shape‐based features are used for the detection of tumor cells. Furthermore, these features are used for the classification of cells into malignant and benign categories using Naive Bayesian and Artificial Neural Network. Moreover, an important phase addressed in the proposed framework is the grading of the affected cells, which could help in grade level necessary medical procedures for patients during the diagnostic process. For demonstrating the e effectiveness of the proposed approach, experiments are performed on real data sets comprising of patients data, which has been collected from the pathology department of Lady Reading Hospital of Pakistan. Moreover, a cross‐validation technique has been performed for the evaluation of the classification accuracy, which shows performance accuracy of 98% as compared to physical methods used by a pathologist for the detection and classification of the malignant cell. Experimental results show that the proposed approach has significantly improved the detection and classification of the malignant cells in breast cytology images.
The research presents a framework which incorporates a cloud‐based decision support system for the detection and classification of malignant cells in breast cancer, while using breast cytology images and shape‐based features to detect tumor cells.