Deep Transfer Learning with Optimal Deep Belief Network Based Medical Image Classification Model

Medical imaging roles an important play in distinct medical applications like medical processes utilized for early recognition, analysis, observing, and treatment evaluation of several clinical conditions. The fundamentals of the rules and executions of artificial neural networks (ANN) and deep learning (DL) are vital to understanding medicinal image analysis from computer vision. A medical image classifier is an essential approach for Computer- Aided Diagnosis (CAD) system. The recent DL approaches offer an effective manner for constructing an end-to-end method which is to calculate last classifier labels with raw pixels of medicinal images. This research gives rise to the MNODBN-MIC model, which stands for MobileNet with optimal deep belief network based medical image classification. There will be multiple class labels applied to the medical images in accordance with the MNODBN-MIC model. The MNODBN-MIC model is able to achieve this objective mainly through the usage of the GF based noise removal methodology. In addition, the MNODBNMIC model finds the impacted areas by determining a graph-cut based segmentation tool. And feature vectors are also generated using the MobileNet model. Combining the DBN model with elephant herd optimisation (EHO) is the last stage in classifying the data. The EHO algorithm is tasked with adjusting the DBN parameters during this procedure. Using a benchmark dataset, we conduct experimental validation of the MNODBN-MIC model, and the findings show that it outperforms other methods that have been used recently.