Underwater photography is frequently used for research purpose in various domains. Domains caters to archaeology, surveillance of aquatic life movements, oceanic changes leading to alterations in weather and many more. Scientists are eager to investigate the mysterious undersea environment. For underwater surveys, archaeology departments and weather forecasting scientists obtain undersea photos. The underwater imagery however has low vision and contrast due to haze. The elimination of haze could be difficult because it depends on depth information that is unclear. Moreover, it’s challenging and complicated to clear the haze so as to enhance the underwater image. According to the investigation, fog removal algorithms currently in use do not take noise reduction approaches into account. Dehazing techniques have a hard time dealing with areas that are unevenly and excessively light. Therefore, it is vital to alter current techniques in order to make them more efficient. This work presents an innovative integrated underwater picture restoration technique. The proposed technique is in line to a pre-determined technique namely Underwater Image Formation Model. The new approach combines Bilateral Filtering, Contrast Limited Adaptive Histogram Equalization and Dark Channel Prior for better results. First, the underwater image undergoes bilateral filtering to eliminate color discrepancies. The improved image is output of the differentiation between forward and background channel. Further, the Contrast Limited Adaptive Histogram Equalizations methodology is used to produce contrast-enhanced images. Experimental results signpost that the proposed novel technique generates superior visual effects compared to other widely used undersea color image quality evaluation techniques.

Glaucoma, a series of progressive eye illnesses, is a primary worldwide health concern. Glaucoma, sometimes known as the "silent thief of sight," progressively affects the optic nerve, resulting in permanent vision loss and, in extreme instances, blindness. It is essential to recognize glaucoma in its earlier stages so that patients can receive treatment sooner and prevent further vision loss. An effective method for detecting glaucoma by analyzing retinal images with the assistance of a deep learning strategy is presented as a potential solution in this article. The framework presented for detecting glaucoma comprises two modules that rely on one another: the Retinal Image Classification Module (RICM) and the Retinal Image Diagnosis Module (RIDM). The retinal image is classified as either a normal or a glaucoma retinal image by the RICM module, which uses the CNN classifier. The RIDM detects the neuro rim region from the glaucoma retinal image by segmenting OD and OC, and the Dual Functional CNN (DFCNN) classifier is proposed to diagnose the severity stages of the glaucoma image based on the feature patterns that are extracted from the neuroretinal rim in the glaucoma image. Both low- and high-resolution retinal image datasets, known as HRF and PAPILA, are utilized in this study to investigate the proposed approaches for glaucoma identification and severity estimate. Compared to other methods considered to be state-of-the-art, the simulation's findings show that it is successful. Ophthalmologists benefit from the suggested model since it assists them in effectively recognizing glaucoma in patients, which in turn allows for improved diagnosis and the prevention of premature vision loss.

The early detection of diabetic ulcers using thermal imaging is an important aspect of non-invasive medical instrumentation. An accurate assessment of a diabetic foot ulcer (DFU) using a machine-based approach requires a crystal-clear region of interest (ROI) of the foot ulcer. Different shapes based on automatic contour determination after the segmentation procedure can act as a major guide for the purpose of appropriate localization of the ROI. The purpose of this paper is to present a novel shape-area-based analysis for precisely localizing the ROI from the patient’s foot. The novel data set, which is suitable for Indian healthcare settings, was created at PGIMER hospital Chandigarh with the support of specialized clinicians. A comparison of various cutting-edge segmentation techniques was carried out. The quantitative analysis concluded that the average area (AA) of ROI, derived from different shapes, was extremely close to the ground truth values and thus offered a better prospective to automatically examine the ulcer area.

The advancement of technology has resulted in a substantial rise in the number of computing devices and the volume of data being transmitted over networks. The need for fast and secure data encryption has become imperative in response to the increase in data transmission and computing devices. In our previous work, we presented a Fisher-Yates Shuffling (FYS) based image encryption algorithm with a timeout feature that ensures improved security and privacy, regardless of key size. However, the implementation was sequential, and it did not fully utilize the multi-core architecture available on modern computer systems. Therefore, this paper seeks to optimize the FYS-based image encryption algorithm’s performance by parallelizing it on a CPU, with the aim of improving its speed without compromising its security and privacy features. The use of Joblib and multithreading are employed to generate the SHA keys, with a quad-core processor with eight logical processors utilized for the research. The parallelization approach has been tested over thousands of images and has been shown to improve the encryption speed by 2 to 5 times compared to the FYS-based image encryption algorithm. The results demonstrate that using CPU parallelization significantly increases the performance of the FYS-based image encryption algorithm.

Neurophysiological parameters revealed by resting-state electroencephalography (rsEEG) may be helpful in the diagnosis of various brain diseases like Epilepsy, Alzheimer’s, depressive disorders, and many others. Due to the abrupt onset of seizures, Epilepsy is a chronic nerve illness that interferes with an epileptic patient's regular everyday activities. However, manual investigation of EEG for finding epileptiform discharges by skilled neurologists is a laborious, time-consuming, and error-prone process. It might cause a significant delay in providing clinical care to a person who could have epilepsy. This work offers a straightforward method for analyzing EEG data for the purpose of identifying epileptic features by iteratively simulating multiple deep learning models. It also attempts to include big data analytics for handling the challenge of analyzing the mountain of unstructured EEG data, available and accessible in numerous formats. In contrast to the state-of-the-art works, the performance scores of the proposed methods show significant improvement for the considered assessment parameters. Additionally, after testifying the performance of this proposed technique for relevant datasets, its application can be extended to identify other neurodegenerative disorders as well. Therefore, this study can assist physicians and healthcare professionals in the efficient care and treatment of patients with mental health issues.

Medical imaging is a field of medicine where doctors use images of different body organs to treat or diagnose patients. Nowadays, medical image segmentation, compression, and security are currently relatively difficult issues for illness diagnosis. These medical pictures are being sent via the internet; thus data must be protected against cyberattacks. Medical images are extremely sensitive to even slight changes, and data volumes are dramatically increasing the amount of the data. To protect the confidentiality of digital images saved online, privacy and security must be ensured.  In this paper, a novel DL-based Generative Adversarial Network (GAN) with tent map and hash-map utilized to generate a robust private key. The fake image is generated by using GAN. T It is suggested to use the 2D-Henon Sine Map (2D-HSM), DNA computing, chaotic maps, and a SHA-512-based strategy are proposed. The SHA-512 algorithm and the 2D-HSM are used to construct the key. The Henon map and the Mersenne Twister are used in a two-level encryption method that is shown (MT). After that, a DNA computing-based XOR operation is performed using the key. A decoding procedure based on DNA rules captures the encoded images. The comprehensive outcome is based on several security measures, such as key space, SSIM, information entropy, PSNR, and histogram analysis. The proposed technique performs better than the existing approaches.

Polycystic Kidney disease (PKD) is often caused due to inherited condition and it forms many cysts around the kidney, and it is damaged when it grow. Accurate segmentation of PKD is very crucial for a persistent MRI diagnostics. Because many people have no symptoms, they can lead to complications until the surgery is done to remove the cyst. Methods: For accurate detection PKD, the heap of MRI images have been considered, In this work, A novel method includes feature based Fuzzy C means (FFCM) with whale optimization algorithm (WOA) for accurate segmentation of kidney cyst. WOA is used to optimally attach the cluster centroids of FCM. In the conventional methods like mountain models and fuzzy C-shells models are used to identify the regions of interest (ROI). Result: The outcomes of FFCM and WOA based process are compared with the results from existing methods using IB-FCM and Fuzzy K-means and FCM model. Conclusion: However, an exact boundary of the region is obtained and  computed an experimental dispersal of the image by Feature extraction based Fuzzy C-Means Clustering segmentation. A detection process is based on the FFCM and WOA segmentation is accomplished to discriminate the normal cyst and the kidney disease. The experimental evaluation is accomplished through the use of Ischemic kidney Disease (IKD) database.

Colon cancer is a growth of cells that begins in a part of the large intestine called the colon. Colon cancer happens when cells in the colon develop changes in their DNA. Consequently, fewer infections and fatalities may result from early identification of this cancer. Histological analysis is used for a final diagnosis of colon cancer. Histopathology, or the microscopic examination of damaged tissue, is crucial for both cancer diagnosis and treatment. This work suggests a novel deep learning technique for colon cancer detection effectively. Histopathology images are collected from various type of sources. To enhance the quality of raw images, pre-processed techniques such as image scaling, colour map improved image sharpening, and image restoration are used. Resize the image's dimensions in image resizing to minimize the processing time. A colour map enhances the sharpness of an image by combining two techniques: The contrast adjustment technique is used to alter the image's contrast first. The resultant image is then enhanced by applying the image sharpening process and scaling it using a weighting fraction. As using the final image has increased quality, blur and undesirable noise are removed using image restoration. Next, the pre-data are used in the Attention U-Net segmentation procedure, which segments the region of the pre-data. To extract features from this segmented image to perform an accurate diagnosis, efficientnetB0 is used. In data extraction, the Bidirectional GRU model is used to process the data further in order to develop predictions. When processing input sequences in both directions with the BiGRU model, it is feasible to gather contextual information to increase accuracy and predict colon cancer effectively. In the proposed model colon disease prediction classifier offer 97% accuracy, 96% specificity and 95.49% F1_score. Thus, the proposed model effectively predicts colon cancer and improves accuracy.