Age-related macular degeneration is a visual disorder caused by abnormalities in a part of the eye’s retina and is a leading source of blindness. The correct detection, precise location, classification, and diagnosis of choroidal neovascularization (CNV) may be challenging if the lesion is small or if Optical Coherence Tomography (OCT) images are degraded by projection and motion. This paper aims to develop an automated quantification and classification system for CNV in neovascular age-related macular degeneration using OCT angiography images. OCT angiography is a non-invasive imaging tool that visualizes retinal and choroidal physiological and pathological vascularization. The presented system is based on new retinal layers in the OCT image-specific macular diseases feature extractor, including Multi-Size Kernels ξcho-Weighted Median Patterns (MSKξMP). Computer simulations show that the proposed method: (i) outperforms current state-of-the-art methods, including deep learning techniques; and (ii) achieves an overall accuracy of 99% using ten-fold cross-validation on the Duke University dataset and over 96% on the noisy Noor Eye Hospital dataset. In addition, MSKξMP performs well in binary eye disease classifications and is more accurate than recent works in image texture descriptors.

The World Health Organization estimates that there were around 10 million deaths due to cancer in 2020, and lung cancer was the most common type of cancer, with over 2.2 million new cases and 1.8 million deaths. While there have been advances in the diagnosis and prediction of lung cancer, there is still a need for new, intelligent methods or diagnostic tools to help medical professionals detect the disease. Since it is currently unable to detect at an early stage, speedy detection and identification are crucial because they can increase a patient’s chances of survival. This article focuses on developing a new tool for diagnosing lung tumors and providing thermal touch feedback using virtual reality visualization and thermal technology. This tool is intended to help identify and locate tumors and measure the size and temperature of the tumor surface. The tool uses data from CT scans to create a virtual reality visualization of the lung tissue and includes a thermal display incorporated into a haptic device. The tool is also tested by touching virtual tumors in a virtual reality application. On the other hand, thermal feedback could be used as a sensory substitute or adjunct for visual or tactile feedback. The experimental results are evaluated with the performance comparison of different algorithms and demonstrate that the proposed thermal model is effective. The results also show that the tool can estimate the characteristics of tumors accurately and that it has the potential to be used in a virtual reality application to “touch” virtual tumors. In other words, the results support the use of the tool for diagnosing lung tumors and providing thermal touch feedback using virtual reality visualization, force, and thermal technology.

Optical coherence tomography (OCT) is a leading imaging technique for diagnosing retinal disorders such as age-related macular degeneration and diabetic retinopathy. Its ability to detect structural changes, especially in the optic nerve head, has made it vital for early diagnosis and monitoring. This paper surveys techniques for ocular disease prediction using OCT, focusing on both hand-crafted and deep learning-based feature extractors. While the field has seen rapid growth, a detailed comparative analysis of these methods has been lacking. We address this by reviewing research from the past 20 years, evaluating methods based on accuracy, sensitivity, specificity, and computational cost. Key diseases examined include glaucoma, diabetic retinopathy, cataracts, amblyopia, and macular degeneration. We also assess public OCT datasets widely used in model development. A unique contribution of this paper is the exploration of adversarial attacks targeting OCT-based diagnostic systems and the vulnerabilities of different feature extraction techniques. We propose a practical, robust defense strategy that integrates with existing models and outperforms current solutions. Our findings emphasize the value of combining classical and deep learning methods with strong defenses to enhance the security and reliability of OCT-based diagnostics, and we offer guidance for future research and clinical integration.

Spectral Domain Optical Coherence Tomography (SD-OCT) is a widely used imaging technique in ophthalmology. However, it often suffers from severe distortion due to speckle noise, which can obscure critical retinal structures and lesions. These distortions can significantly reduce accuracy of image-based diagnostic tasks. Developing effective techniques for reducing speckle noise and improving the quality of SD-OCT images is crucial. However, there are two main challenges in removing speckle noise: (1) balancing the removal of noise while preserving essential image details, and (2) that speckle noise can have varying intensity and size levels, making it challenging to develop a onesize-fits-all approach. If too much noise is removed, the image may become overly smoothed and lose essential details. On the other hand, if noises are not sufficiently removed, the image may still appear noisy and distorted. Different methods and algorithms may need to be used depending on the noise characteristics and the specific image being processed. Despite these challenges, various denoising techniques, such as wavelet-based, non-local means, and adaptive median filtering, have been proposed in the literature. Each method has its strengths and weaknesses, and the choice of the method should be based on the noise characteristics and the desired trade-off between noise removal and image preservation. While recent works in deep learning have shown promise in denoising OCT images, they require extensive training data and complex hardware, limiting their practicality in many settings. This paper presents an edge-preserving noise removal method for improving the quality of SD-OCT by reducing the effect of noise using a new morphology-based bitonic filter. This filter is created by combining extended Okada with various kernel sizes. This approach allows us to efficiently remove speckle noise from OCT images while minimizing the loss of details and enhancing image quality. Compared to existing methods, the presented approach is more efficient and requires fewer computational resources. It could enhance the accuracy of image-based diagnostic tasks, ultimately benefiting patients and clinicians alike.

Gastrointestinal (GI) diseases if undiagnosed can lead to severe health complications, including malnutrition, dehydration, and even cancer. Colonoscopy, the gold standard for GI disorder diagnosis, often faces challenges such as low-contrast images due to poor illumination, making it difficult to detect fine details crucial for accurate diagnosis. Additionally, AI-based healthcare, including GI Disease Detection systems, is susceptible to adversarial attacks, raising significant concerns for patient safety due to potential misclassifications in clinical decision-making. To mitigate these issues, we introduce a novel Computer Assisted Diagnostics (CAD) system for GI disease detection and resilience against adversarial threats. Our approach incorporates two key innovations: first, a preprocessing technique using Quaternion Dark Channel Laplacian of Gaussian (QDC-LoG) for edge and contrast improvements; second, an ensemble of Convolutional Neural Networks (CNNs) and classifiers for robust GI disorder prediction. Through comprehensive experiments, we demonstrate that our method not only surpasses current state-of-the-art techniques, including advanced deep learning models and transformers, by achieving a 97.88% accuracy on the Wireless Capsule Endoscopy (WCE) Curated Colon Disease Dataset, but also shows exceptional specificities on Kvasir V1 and V2 datasets. Furthermore, our system exhibits strong resilience against Contrast Adversarial Degradation Attacks (CADA), ensuring reliable performance even under severe contrast reduction.

Optical coherence tomography (OCT) is a leading imaging technique for diagnosing retinal disorders such as age-related macular degeneration and diabetic retinopathy. Its ability to detect structural changes, especially in the optic nerve head, has made it vital for early diagnosis and monitoring. This paper surveys techniques for ocular disease prediction using OCT, focusing on both hand-crafted and deep learning-based feature extractors. While the field has seen rapid growth, a detailed comparative analysis of these methods has been lacking. We address this by reviewing research from the past 20 years, evaluating methods based on accuracy, sensitivity, specificity, and computational cost. Key diseases examined include glaucoma, diabetic retinopathy, cataracts, amblyopia, and macular degeneration. We also assess public OCT datasets widely used in model development. A unique contribution of this paper is the exploration of adversarial attacks targeting OCT-based diagnostic systems and the vulnerabilities of different feature extraction techniques. We propose a practical, robust defense strategy that integrates with existing models and outperforms current solutions. Our findings emphasize the value of combining classical and deep learning methods with strong defenses to enhance the security and reliability of OCT-based diagnostics, and we offer guidance for future research and clinical integration.