No abstract
The fact that pancreatic cancer has a low life expectancy, that is only 9% of people survive five years, makes a diagnosis catastrophic. The majority of patients are diagnosed late in life, where care choices are minimal. Early diagnosis of pancreatic cancer will greatly increase a person’s chances of survival. Accurate PC staging will help doctors have the right treatment plan for PC patients at different stages, as well as the diagnostic measures needed for a quicker cancer recovery. In this proposed project, ultrasound images will be analyzed. The noise in the image is minimised using the Median Filter. In the next step, Gray Level Co-occurrence Matrix (GLCM) and Discrete Wavelet Transform (DWT)are used to extract related features. Following this extraction step, the refined characteristics are fed into a Probabilistic Neural Network (PNN) neural network classifier, which determines whether or not cancer is present. Metrics such as sensitivity, precision, and specificity are used in experimental computation.
Glaucoma which is known as the “thief of sight”, is an irreversible eye disease It is mainly caused by increased intraocular pressure (IOP), or loss of blood supply to the optic nerve. Glaucoma detection and diagnosis is very important. By analyzing the optic disc and its surroundings, This paper introduces a method for providing automated glaucoma screening services based on a framework that proposes a retinal image synthesizer for glaucoma assessment by analyzing the optic disc and its surroundings. The Cup to Disc Ratio (CDR) is critical for the system, and it is calculated using 2-D retinal fundus images. The synthetic images produced by our system are compared quantitatively. The structural properties of synthetic and real images are analyzed, and the quality of colour is calculated by extracting the 2-D histogram. The system allows patients to receive low-cost remote diagnostics from a distance, preventing blindness and vision loss by early detection and management.
Due to the picture similar region of interest, local area, and the effect of respiration, separating a lung tumor from neighboring tissue from a collection of magnetic resonance images (MRI) faces a number of challenges. However, precise tumor segmentation is needed when preparing radiation therapy in order to prevent excessive radiation exposure to healthy tissues. Delineation of the whole MRI pattern by hand is boring, time-consuming, and costly. Using neural networks, this research studies the automated tracking of tumor borders during radiation therapy. To improve the accuracy of lung tests, we proposed using neural network architecture with fuzzy clustering.
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