An Intelligent Black Widow Optimization on Image Enhancement with Deep Learning Based Ovarian Tumor Diagnosis model

Ovarian tumour is a commonly affecting gynaecologic malignancy which necessitates effective image processing techniques for accurate diagnosis. This article presents an intelligent IE with a deep learning-based ovarian tumour diagnosis (IEDL-OVD) model. The goal of the IEDL-OVD model is to enhance the quality of the input medical image, thereby improving the diagnostic outcomes. The proposed IEDL-OVD model includes a black widow optimisation-based IE technique. In addition, the VGG16 model is applied as a feature extractor and a stacked autoencoder (SAE) is utilised as a classification model to determine the existence of the ovarian tumour. In order to inspect the diagnostic outcome of the IEDL-OVD model, an elaborative experimentation analysis is performed and the results are examined in terms of different evaluation parameters. With 100 images, the IEDL-OVD model has obtained an increased precision rate and recall rate of 0.735 and 0.612. The black widow optimization algorithm (BWOA) on the quality IE has achieved a maximum contrast of 0.97, a contrast-to-noise ratio (CNR) of 92.74%, a weighted peak signal-to-noise ratio (WPSNR) of 20.43 and a homogeneity of 0.94.