White Blood Cell (WBC) cancer or leukemia is one of the serious cancers that threaten the existence of human beings. In spite of its prevalence and serious consequences, it is mostly diagnosed through manual practices. The risks of inappropriate, sub-standard and wrong or biased diagnosis are high in manual methods. So, there is a need exists for automatic diagnosis and classification method that can replace the manual process. Leukemia is mainly classified into acute and chronic types. The current research work proposed a computer-based application to classify the disease. In the feature extraction stage, we use excellent physical properties to improve the diagnostic system's accuracy, based on Enhanced Color Co-Occurrence Matrix. The study is aimed at identification and classification of chronic lymphocytic leukemia using microscopic images of WBCs based on Enhanced Virtual Neural Network (EVNN) classification. The proposed method achieved optimum accuracy in detection and classification of leukemia from WBC images. Thus, the study results establish the superiority of the proposed method in automated diagnosis of leukemia. The values achieved by the proposed method in terms of sensitivity, specificity, accuracy, and error rate were 97.8%, 89.9%, 76.6%, and 2.2%, respectively. Furthermore, the system could predict the disease in prior through images, and the probabilities of disease detection are also highly optimistic.
Today, sensors generate vast amounts of data in different fields such as hospitals, the transport sector, social media, and so on. In hospitals, the use of sensors that are installed in the patient’s body to monitor the pulse rate, heartbeats, head movement, eyes, and other body
parts. Every day, these collected data are stored in local data servers and database servers by various sensors that require effective handling of these data. Sensors are primarily used in most of the IoT applications in everyday life from which smart city plays a crucial role. The aim of
the work is to address the application of big data in healthcare and life science, including different types of data that involve special attention in processing. This work focuses on the use of large-data analytical techniques to process medical data. A large volume of unstructured cancer
database is considered to identify and predict different types of cancer such as breast cancer, lung cancer, blood cancer, and so forth. This research involves the segmentation of thousands of records on cancer forms in a broad cancer database into various segmented databases. Using KNN algorithm
this segmentation, classification and prediction will be achieved.
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