Use of Cutting-Edge Deep Learning Algorithms in Combination with Bioinformatics to Detect Rare Brain Tumors
Keywords:
Nitroglycerine, Neural Networks, data augmentation, image processing approachesAbstract
Integrative bioinformatics can research nitro-glycerine’s several pathways of action in various cancers to better understand its effects. Throughout our inquiry, we relied on public resources. This study's initial stage is to identify the genes that are linked together. The nitroglycerine target genes were discovered by the use of PubChem. The Closeness Coefficient and effect on cancers of 12 direct target genes were examined in a PPI network. The CluePedia App was used to study the activity of biomolecules in particular genes. There was no doubt about the connection between certain types of cancer and certain types of gene alterations. The PPI network was used to discover the types of tumours that were impacted by 12 target genes. Even in a developed country like the United States, where haematologists and oncologists are plentiful, there is a doctor-to-patient ratio of 1:20,366. Think about what it would entail if it were implemented worldwide. Recent years have seen tremendous advancements in the medical industry, and visual and image recognition technology is now widely used across various fields for a number of purposes. AI researchers are actively studying neural networks (NNs) and related ideas. This study employed data augmentation and image processing to develop a CNN. The CNN model was compared to the VGG-16 architecture to see whether it could detect these flaws better. Our model outperformed the VGG-16 in this detection test despite using minimal training data. However, the VGG-16 model uses more memory and compute. Reducing unidentifiable data is another advantage of this research. Study highlights how we may leverage user consent to gather and retain data for future educational and medical researchers and retrain algorithms for better outcomes.
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