An efficient approach for the detection of multiple cancer using deep learning

Abstract

Our era is witnessing increasing pressure on the quality and quantity of healthcare due to newlinethe increase in consumption of tobacco and excessive exposure to harmful radiation, newlinechronic diseases, and the health consciousness of people. According to experts in the newlinemedical field, the ability to detect and diagnose diseases at an early stage is of the utmost newlinesignificance. On the one hand, this ability helps to effectively slow the progression of newlineillness, and on the other hand, this ability helps to significantly reduce the cost of newlinehealthcare systems. newlineCancer is becoming a serious illness having a high mortality range. Cancer is a disease newlinethat causes death even before the age of 60, according to recent research conducted in newline90 nations. More than 9 million individuals will pass away in 2020, with cancer newlineaccounting for 1 in 6 of those fatalities. Skin and oral malignancies are considered the newlinegreatest prevalent cancer types. Predicting the cancerous type based on pictures is crucial. newlineThis research work introduces revolutionary deep-learning methodologies for skin and newlineoral cancer diagnosis at the start of the disease. The main goal of this research work is to newlineassist physicians in early oral and skin cancer diagnosis. Convolutional neural networks newlineand VGG19 are combined in the proposed model to identify and classify various forms of newlineskin and oral cancer. The author pares the dataset with other competing classifiers to newlinevalidate the algorithm. The findings demonstrate that these methodologies hybridization newlineprovides greater accuracy than conventional techniques. The proposed model has a newline97.17% accuracy, a 92.19% precision, a 92.18% recall, and a 93.10% F1- score va newline