Medical Imaging is the most significant technique that constitutes information needed to diagnose and make the right decisions for treatment. These images suffer from inadequate contrast and noise that occurs during image acquisition. Thus, denoising and contrast enhancement is crucial in increasing the visual quality of the images for obtaining quantitative measures. In this research, an innovative and improvised denoising technique is implemented that applies a sparse aware with convolution neural network (SA_CNN) for investigating various medical modalities. To evaluate and validate, the convolution neural network utilizes patch creation and dictionary methods for obtaining information. The proposed framework is predominant to other current approaches by employing image assessment quantitative measures like peak signal to noise ratio (PSNR), structural similarity index (SSIM), and mean squared error (MSE). The study also optimizes the computational time to achieve increased efficiency and better visual quality of the image. Furthermore, the widespread use of the Internet of Healthcare Things (IoHT) helps to provide security with vault and challenge schemes between IoT devices and servers.

Kidney failure occurs whenever the kidney stops to operate properly and would be unable to cleanse or refine the bloodstream as it should. Chronic kidney disease (CKD) is a potentially fatal consequence. If this condition is diagnosed early, its progression can be delayed. There are various factors that increase the likelihood of developing kidney failure. As a consequence, in order to detect this potentially fatal condition early on, these risk factors must be checked on a regular basis before the individual’s health deteriorates. Furthermore, it lowers the cost of therapy. The chronic kidney or renal disease will be recognized in this work utilizing fuzzy and adaptive neural fuzzy inference systems. The fundamental purpose of this initiative is to enhance the precision of medical diagnostics used to diagnose illnesses. Nephron functioning, glucose levels, systolic and diastolic blood pressure, maturity level, weight and height, and smoking are all elements to consider while developing a fuzzy and adaptable neural fuzzy inference system. The output variable describes a specific patient’s stage of chronic renal disease based on input factors such as stage 1, stage 2, stage 3, stage 4, and stage 5. The outcome will show the present stage of a patient’s kidney. As a result, these methods can assist specialists in determining the stage of chronic renal disease. MATLAB software is used to create the fuzzy and neural fuzzy inference systems.

Magnetic resonance imaging (MRI) is precise and efficient for interpreting the soft and hard tissues. Moreover, for the detailed diagnosis of varied diseases such as knee rheumatoid arthritis (RA), segmentation of the knee magnetic resonance image is a challenging and complex task that has been explored broadly. However, the accuracy and reproducibility of segmentation approaches may require prior extraction of tissues from MR images. The advances in computational methods for segmentation are reliant on several parameters such as the complexity of the tissue, quality, and acquisition process involved. This review paper focuses and briefly describes the challenges faced by segmentation techniques from magnetic resonance images followed by an overview of diverse categories of segmentation approaches. The review paper also focuses on automatic approaches and semiautomatic approaches which are extensively used with performance metrics and sufficient achievement for clinical trial assistance. Furthermore, the results of different approaches related to MR sequences used to image the knee tissues and future aspects of the segmentation are discussed.

Spam is also known as junk mail or Unsolicited Commercial Email (UCE) which has become major problem for the sustainability of the internet and global commerce. Everyday millions of the spam mails are sent over internet to targeted population to advertise services, products and dangerous software etc. A number of spam detection algorithms have been proposed to classify emails on content based, but could not gain accuracy. Our proposed work mainly focuses on cognitive (spam) words for classification. This feature is sequential unique and closed patterns which are extracted from the message content. We show that this feature have good impact in classifying spam from legitimate messages. Our method, which can be easily implemented, compares amiably with respect to popular algorithms, like Logistic Regression, Neural Network, Naive Bayes and Random Forest using polynomial kernel as filter. We outperform the accuracy higher compared to related methods. In addition our method is resilient against irrelevant and bothersome words.