In the modern world, cataracts are the predominant cause of blindness. Early treatment and detection can reduce the number of cataract patients and prevent surgery. However, cataract grade classification is necessary to control risk and avoid blindness. Previously, various studies focused on developing a system to detect cataract type and grade. However, the existing works on cataract detection does not provide optimal results because of high detection error, lack of learning ability, computational complexity issues, etc. Therefore, the proposed work aims to develop an effective deep learning techniques for detecting and classifying cataracts from the given input samples. Here, the cataract detection and classification are performed using two phases. In order to provide an accurate cataract detection, the proposed study introduced Deep Optimized Convolutional Recurrent Network_Improved Aquila Optimization (Deep OCRN_IAO) model in phase I. Here, both retinal and slit lamp images are utilized for cataract detection. Then, the performance of these two image datasets are analysed, and the best one is chosen for cataract type and grade classification. By analysing the performance, the slit lamp images attain higher results. Therefore, phase II uses slit lamp images and detects the type and grade of cataracts through the proposed Batch Equivalence ResNet-101 (BE_ResNet101) model. The proposed classification model is highly efficient to classify the type and grades of cataracts. The experimental setup is done using MATLAB software, and the datasets used for simulation purposes are DRIMDB (Diabetic Retinopathy Images Database) and real-time slit lamp images. The proposed type and grade detection model has an accuracy of 98.87%, specificity of 99.66%, the sensitivity of 98.28%, Youden index of 95.04%, Kappa of 97.83%, and F1-score is 95.68%. The obtained results and comparative analysis proves that the proposed model is highly suitable for cataract detection and classification.
Denoising and contrast enhancement techniques in normal and slit lamp images are challenging in medical analysis. These images are essential in medical analysis for preventing, diagnosing, and monitoring various diseases. For this purpose, the medical images should be clear. Hence, this work proposes an optimized deep learning model to provide denoised and contrast-enhanced images. To begin, the selection of picture quality is merged to identify high-quality normal as well as slit lamp images for subsequent analysis. The picture quality of an image is examined with the model “Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE)”. It yields a low score for pictures of high-quality, an average score for pictures of medium quality, as well as a high score for images of poor quality. Furthermore, normalization-based contrast limited adaptive histogram equalisation (N-CLAHE) is a new algorithm for converting medium- and low-quality images to high-quality images. The images are then pre-processed to remove noise using wiener filtering (WF) with a convolutional neural network (CNN) with adaptive atom search optimization (CNN-AASO). Further, the denoised image is enhanced by Gaussian mixture-based contrast enhancement (GMCE) for contrast enhancement. The overall implementation is carried out on the Matlab tool.
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