The aim of this paper is to analyze and predict ovarian cancer in women using Artificial Intelligence. The program in logic and the decision tree of machine learning are being created to presume Ovarian Cancer. Ovarian malignancy is a significant infection among ladies, even at a very early age. The side effects of ovarian diseases are taken as the factors to settle on the choice tree to foresee the conceivable outcomes. The fundamental side effects would be the foundations of the sickness to settle on the choice tree furthermore than all the yes and no of the tree would have a determination or an outcome. This will assist the women to aware of the type of the ovarian cancer with symptoms and to take necessary steps to avoid this deadly disease. As per the research outcome, it is quite helpful for women all over the world to be aware of the disease. Analysis and prediction provide a major outcome of this research. Advanced technology helps move the health system in a new direction. It gives attention to ladies about ovarian malignancy from one side of the planet to the other. There are numerous country regions all around the world exists where the specialist and the patient proportion are poor, there it can furnish attention to ovarian malignancy alongside the expectation if any patient has ovarian disease or not. Any little or big indications of ovarian disease, they will become more acquainted with what sort of ovarian malignant growth they have through the product. It will decrease the mortality rate.
Refractive laser surgery is all about the accuracy, whether screening or surgery, given the age and profile of the patient enduring these trials, there is no margin for error. Most of them are for aesthetic reasons, contact lens intolerance, or professional reasons, including athletes. In this article, the role of artificial intelligence and deep learning in laser eye surgeries has been introduced. The presence of lingering laser spots on the retina after refractive laser surgery in diabetic retinopathy poses a potential risk to visual integrity and ocular well-being. The hypothesis for the research paper is that the hybridized convolutional neural network models, including LeNet-1, AlexNet, VGG16, PolyNet, Inception V2, and Inception-ResNetV2, will yield varying levels of performance in classifying and segmenting laser spots in the retina after diabetic retinopathy surgery. The hypothesis predicts that Inception-ResNetV2 will demonstrate superior results compared to the other CNN versions. The research aims to provide a novel approach for laser therapies and treatments, facilitating the rapid classification, highlighting, and segmentation of laser marks on the retina for prompt medical precautions. The comparative analysis revealed that Inception-ResNetV2 exhibited exceptional performance in both training and validation, achieving the highest accuracy (96.54%) for classifying diabetic retinopathy images. Notably, VGG16 also demonstrated strong performance with a validation accuracy of 94%. Conversely, LeNet-1, AlexNet, PolyNet, and Inception V2 displayed comparatively lower accuracy rates, suggesting their architectures may be less optimized for this particular image classification task. This achievement holds immense promise for timely detection, precise localization, and optimal management of laser spots, fostering enhanced visual outcomes and elevating the standards of patient care in this context.
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