Lung Cancer Detection and Recognition using Deep Learning Mechanisms for Healthcare in IoT Environment
Keywords:
IoT, Machine learning, Lung cancer, Accuracy, Performance, CNN, F1 score, recall, precisionAbstract
The use of machine learning in IoT-based healthcare applications has emerged as a prominent field of study, particularly in the realm of predicting chronic diseases. There is a general consensus among individuals that melanoma is regarded as most lethal illnesses. In order to enhance the probability of achieving a cure prior to the onset of cancer, a meticulous classification of lung lesions at their first stages might potentially facilitate the deliberation of therapeutic interventions. Body parts that are often exposed to the deleterious effects of direct sunlight have an elevated susceptibility to the development of lung cancer. However, it may also appear in places of your body that are seldom exposed to air and light, such as your hands, feet, and other areas. To provide algorithms for detection and classification, deep learning has been widely employed as a subfield of machine learning. In this investigation, the deep learning approach is being examined as a way to identify Lung cancer more quickly and accurately. Accuracy metrics including recall, precision, and f1-score were enhanced using hybrid deep learning models, while compression operations were applied to boost speed. People living in the Internet of Things era may benefit from constant, real-time Lung health monitoring. Lung cancer detection and identification inside the IoT has tremendous potential for improving healthcare and preventative methods as ML algorithms continue to advance and acquire access to huge datasets.
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