Body scanner sensor assessment using artificial intelligence.

Abstract

An autonomous scanner sensor recognition system depending on the DenseNet201 Transfer Learning model is recommended in the present research. consumption of sensor image is significantly influenced by the image's condition. If we consume sensor image that is not fresh, it might damage the immune system of the human body and create several kinds of diseases. Therefore, it is imperative that we ingest scanner sensor. Since it is initially exceedingly challenging to distinguish between fresh and sick image manually observing scanner, we propose in this study to replace the monitoring tactic with an automated computer program. The goal of this study is to distinguish between fresh and sick image by evaluating how they seem on the outside. The factor that affects the prediction results in this study is the collection of datasets before the training process is carried out consisting of scanner image samples obtained from the Cumilla medical college. For categorizing image freshness, we divided our six different bespoke datasets for sensor image into three categories using a variety of transfer learning models. However, the scanner sensor image dataset, which had an accuracy score of 97.10%, showed DenseNet201 to be incredibly effective. Therefore, the goal of this study is to precisely determine an image's freshness conditions while minimizing dependency on human vision.