Big Data Analytics for Improved Risk Management and Customer Segregation in Banking Applications
Volume-3 | Issue-3
Design of Deep Learning Algorithm for IoT Application by Image based Recognition
Volume-3 | Issue-3
Analysis of Serverless Computing Techniques in Cloud Software Framework
Volume-3 | Issue-3
Health Record Management System – A Web-based Application
Volume-3 | Issue-4
A Novel Signal Processing Based Driver Drowsiness Detection System
Volume-3 | Issue-3
IoT Based Monitoring and Control System using Sensors
Volume-3 | Issue-2
Secure Data Sharing Platform for Portable Social Networks with Power Saving Operation
Volume-3 | Issue-3
Review of Internet of Wearable Things and Healthcare based Computational Devices
Volume-3 | Issue-3
Stock Index Prediction with Financial News Sentiments and Technical Indicators
Volume-4 | Issue-3
Hybrid Framework on Automatic Detection and Recognition of Traffic Display board Signs
Volume-3 | Issue-3
Suspicious Human Activity Detection System
Volume-2 | Issue-4
ROBOT ASSISTED SENSING, CONTROL AND MANUFACTURE IN AUTOMOBILE INDUSTRY
Volume-1 | Issue-3
EFFICIENT RESOURCE ALLOCATION AND QOS ENHANCEMENTS OF IOT WITH FOG NETWORK
Volume-1 | Issue-2
Live Streaming Architectures for Video Data - A Review
Volume-2 | Issue-4
IoT Based Monitoring and Control System using Sensors
Volume-3 | Issue-2
Big Data Analytics for Improved Risk Management and Customer Segregation in Banking Applications
Volume-3 | Issue-3
A Novel Signal Processing Based Driver Drowsiness Detection System
Volume-3 | Issue-3
IoT BASED AIR AND SOUND POLLUTION MONITIORING SYSTEM USING MACHINE LEARNING ALGORITHMS
Volume-2 | Issue-1
Analysis of Serverless Computing Techniques in Cloud Software Framework
Volume-3 | Issue-3
Hybrid Intrusion Detection System for Internet of Things (IoT)
Volume-2 | Issue-4
Volume - 4 | Issue - 1 | march 2022
Published
16 May, 2022
Medical diagnosis, notably concerning tumors, has been transformed by artificial intelligence as well as deep neural network. White blood cell identification, in particular, necessitates effective diagnosis and therapy. White Blood Cell Cancer (WBCC) comes in a variety of forms. Acute Leukemia Lymphocytes (ALL), Acute Myeloma Lymphocytes (AML), Chronic Leukemia Lymphocytes (CLL), and Chronic Myeloma Lymphocytes (CML) are white blood cell cancers for which detection is time-consuming procedure, vulnerable to sentient as well as equipment blunders. Despite just a comprehensive review with a competent examiner, it can be hard to render a precise conclusive determination in some cases. Conversely, Computer-Aided Diagnosis (CAD) may assist in lessening the number of inaccuracies as well as duration spent in diagnosing WBCC. Though deep learning is widely regarded as the most advanced method for detecting WBCCs, the richness of the retrieved attributes employed in developing the pixel-wise categorization algorithms has a substantial relationship with the efficiency of WBCC identification. The investigation of the various phases of alterations related with WBC concentrations and characteristics is crucial to CAD. Leveraging image handling plus deep learning technologies, a novel fusion characteristic retrieval technique has been created in this research. The suggested approach is divided into two parts: 1) The CMYK-moment localization approach is applied to define the Region of Interest (ROI) and 2) A CNN dependent characteristic blend strategy is utilized to obtain deep learning characteristics. The relevance of the retrieved characteristics is assessed via a variety of categorization techniques. The suggested component collection approach versus different attributes retrieval techniques is tested with an exogenous resource. With all the predictors, the suggested methodology exhibits good effectiveness, adaptability, including consistency, exhibiting aggregate categorization accuracies of 97.57 percent and 96.41 percent, correspondingly, utilizing the main as well as auxiliary samples. This approach has provided a novel option for enhancing CLL identification that may result towards a more accurate identification of malignancies.
KeywordsALL AML CLL CML WBCC deep learning CNN ROI
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