Diabetic retinopathy (DR) syndrome affects the vision of the eyes by damaging the blood vessels. Fore-hand detection and prevention of this syndrome are most significant as it results in vision blindness. Diagnosis and procedural analysis of this syndrome with modern healthcare science and technology are aided through artificial intelligence and processing units. In this article, a threshold segmentation based DR detection method is introduced. This method is keen is classifying the foreground and background of the input retinal image and processing through pixel-based segmentation. The process of assessing the layers is augmented using a two-layer convolutional neural network (CNN) that mitigates the false positives during classification. This process is sequential in determining the precise detection of the infected region of the retina. Besides, the segment-based CNN (S-CNN) handles the flaw in diagnosis through two-hidden layers for differentiating the threshold and normalized conditions based on classification. The proposed method is reliable in achieving better accuracy of detection, sensitivity, and true positives.

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Gliomas are one of the most prevalent and aggressive form of brain tumours in the world. Patient's usually go on to live a very short life after the initial diagnosis. Therefore, it is crucial to successfully and quickly outline a method for diagnosing the same in it's very earliest stages.Magnetic Resonance Imaging, or MRI as it is more frequently called is a noninvasive method of imaging parts of human anatomy. MRI's utilise robust fields of magnetism, along with waves that have frequencies corresponding to the radio waves in the spectrum to develop precise pictures to get a sense of the happenings inside the human body. The current, most widely used method of diagnosis for brain gliomas involves an oncologist or radiologist reading the MRI image and using his knowledge and experience regarding the same to reach a diagnosis. However, this manual method of diagnosis is very tedious and has been prone to errors in the past. Therefore, it essential to develop an automatic method for the same.Most of the techniques used currently for segmenting brain tumours were initially developed for other diseases, the most common use among them being the separation of white matter lesions. Most of the current methodologies can be broadly categorised into two families- 1.General Probabilistic Methods- Probabilistic methods are a remarkable method to establish the validity of combinatorial entities with distinct characteristics. Although the basis of their existence lies in probability, they are not bounded by it and can be used to solve and evaluate theorems across different branches of Mathematics.2.Discriminative Approaches- They are also sometimes referred to as Conditional Models. We utilise CNN's for faster and accurate processing of the data.