Prediction of Cancer Blood Disorder Using Adaptive Otsu Threshold and Deep Convolutional Neural Networks:

S. Jeyalaksshmi Vels Institute of Science, Technology, and Advanced Studies, India S. K. Piramu Preethika Vels Institute of Science, Technology, and Advanced Studies, India J. Grace Hannah Vels Institute of Science, Technology, and Advanced Studies, India S. Sathya Vels Institute of Science, Technology, and Advanced Studies, India Sangeetha Radhakrishnan Vels Institute of Science, Technology, and Advanced Studies, India S. Silvia Priscila Bharath Institute of Higher Education and Research, India Prediction of Cancer Blood Disorder Using Adaptive Otsu Threshold and Deep Convolutional Neural Networks

Cancer blood disorder affects blood cell formation and function. Blood disorders may affect platelets, plasma, and white and red blood cells. The goal of this study is to identify blood problems with cancer. In this study, cancer and blood problem images are enhanced and filtered. Remove noise from photos using image filtering. The authors recommended an adaptive anisotropic diffusion filter (2D AADF) for noise reduction. Image enhancement improves clarity. Enhancement uses de-noised photos. The authors propose picture improvement using adaptive mean adjustment (AMA). Real-time data was used for picture preprocessing. The proposed filtering approach is the most effective compared to 2D AADF, 2D adaptive log color filter, and 2D frequency domain filter. The suggested image improvement algorithm performed best compared to contrast limited adaptive histogram equalization, adaptive otsu threshold, image coherence improvement, and 2D adaptive mean adjustment.
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