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.
