In the medical field, some specialized applications are currently being used to treat various ailments. These activities are being carried out with extra care, especially for cancer patients. Physicians are seeking the help of technology to help diagnose cancer, its dosage, its current status, cancer classification, and appropriate treatment. The machine learning method developed by an artificial intelligence is proposed here in order to effectively assist the doctors in that regard. Its design methods obtain highly complex cancerous inputs and clearly describe its type and dosage. It is also recommending the effects of cancer and appropriate medical procedures to the doctors. This method ensures that a lot of doctors’ time is saved. In a saturation point, the proposed model achieved 93.31% of image recognition, 6.69% of image rejection, 94.22% accuracy, 92.42% of precision, 93.94% of recall rate, 92.6% of F1-score, and 2178 ms of computational speed. This shows that the proposed model performs well while compared with the existing methods.
Objectives: To improve quality of images from video capture under normal illumination through SMART system and the best performance in the task of retina blood vessel segmentation with minimize segmentation loss and recover high resolution feature and makes it possible to evaluate high resolution image. Methods analysis: Existing research were showed for spontaneous segmentation of retina blood vessel from fundus images through supervised and unsupervised techniques. On the other hand, most of the research absence in segmentation robustness and cannot enhance loss functions so that results of the segmentation have made lots of fake. In our research, supervise the value of segmentation loss functions for a number of iterations and supports measure the accuracy of Super Resolution Generative Adversarial Network (SRGAN) method in training process using DRIVE dataset. Findings: We enhanced the AUC of 0.9943 %, Sensitivity of 0.8352 % and specificity of 0.9849 % using through SRGAN-UNet method. We additionally applied overlap tile technique for validation which made it conceivable to segment high resolution with overall precision 0.9736%. Novelty: Our proposed method to produce new-fangled, imitation occurrences of data that can pass for real data processing method that make high resolution images from experimental lower solution images based U-Net.
Objectives: To present a real-time algorithm that combines Yolov5 and UNetbased CNN predictions to classify small-sized images, particularly medical images. Methods: The proposed model combines the various phases of preprocessing, object detection, segmentation and classification through Kalman filter, Yolov5, U-net based on CNN. The model is derived from the three different datasets to create a novel classification algorithm for medical data. The dataset contains images of 136 glaucoma patients and 187 healthy images. Findings: The proposed Y-UNet classifier framework is used to classify glaucoma images based on their IOP and CCT from real-time dataset collection.The proposed framework accuracy of 98.75% is achieved in the run time performance at 0.18 seconds per image. The accuracy is higher by 1.66% and runtime performance reduced by 0.03 seconds when compared with standard classification methods. Additionally, threshold optimizer has minimized the overall losses and provides the most accurate result. Novelty: The Y-UNet classifier model proposed here enables the real-time glaucoma prediction tasks to be carried out through the use of a hardware-based system known as the Sensitive Mirror Analyzer and Retina Tracker. To the best of our knowledge, for the first time, the proposed algorithm has been implemented to improve the accuracy and run time performance.
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