Dementia is the world's most deadly disease. A degenerative disorder that affects the thinking, memory, and communication abilities of the human brain. According to World Health Organization, more than 40 million people worldwide suffer from this illness. One of the most common methods for analyzing the human brain, including detecting dementia, is using MRI (Magnetic resonance imaging) data, which provides insight into the inner working of the human body. Using MRI images a deep Convolution neural network was designed to detect dementia, we are utilizing image processing to help doctors detect diseases and make decisions on observation, in an earlier stage of the disease. In this paper, we are going to get to the bottom of the DenseNet-169 model, to detect Dementia. There are approximately 6000 brain MRI images in the database for which the DenseNet-169 model has been used for classification purposes. It is a Convolution Neural Network (CNN) model that classifies Non-Dementia, Mild Dementia, Severe Dementia, and Moderate Dementia. The denseNet-169 model helps us determine Dementia disease. And also present the 97% accuracy for clarification of disease is present in the patient body. we are conducted this survey for providing effective disease prediction model for physicians to conclude that the disease stage is accurate and provide proper treatment for that.  

Skin Lesion is a part of the skin that can be caused by abnormal growth in the epithelium layer on the skin. There are nine types of skin lesion like Actinic Keratoses (AK), Basal Cell Carcinoma (BCC), Dermatofibroma (DF), Melanoma (MEL), Melanocytic Nevi (MV), Benign Keratosis (BK), Vascular Lesions (VASC), Squamous Cell Carcinoma (SCC), and Pigmented Benign Keratosis (PBK). The aim of this study is to spotlight on the problem of skin lesion classification based on early detection of the disease using deep learning techniques. This approach is used to work out the problem of classifying a dermoscopic image. The dermoscopic is a digital device; in this case Smartphone is attached to a lens and collects the images through the device. The proposed spotlight is built in the region of using Convolutional neural network architecture and ResNet-50 module is used to predict Skin-Lesion classification. The dataset used in this research was taken from kaggle repository. The proposed work uses ResNet-50 CNN model which has yielded 93% of accuracy for detecting Skin Cancer, previous work was carried out using Visual Geometry Group model which yielded 73% accuracy. In the proposed work we have considered 25,000 images of skin lesion. Hence we are able to attain this accuracy with more reliable Machine Learning algorithms compared to the previous work.

A Drug-Drug Interaction is an alteration in the impact of a drug when consolidated with another drug or group of drugs. Drug interactions are common and have caused increased hospital admission rates, treatment failures, avoidable medical complications, and even deaths. Studies have found multiple drug usage, and age-related comorbidities to be the reasons for the interactions and this demands a general study. Here in this paper, we discuss the Drug-Drug Interactions between Chronic Obstructive Pulmonary Disease (COPD) and its associated comorbidity diseases. We have designed a Drug Decision Support System which helps the Physicians to check the Drug-Drug interaction between Chronic Obstructive Pulmonary Disease and its associated comorbidity diseases. COPD is a fourth decade disease means after age 40 it may be diagnosed and is currently fourth largest killing disease. Study says one of the major cause for COPD is smoking (active/passive). As there is no cure for COPD yet. The patient’s life can be improved by providing better treatment and management strategies. Once the patient is diagnosed with COPD the patient may also end up suffering with the comorbidity diseases associated with COPD like Asthma, Depression, Dementia, Diabetics, Heart Failure, Hypertension, Hypotension, Obesity, and Osteoporosis. The patient has no choice but taking the prescribed drugs for COPD and other comorbidity disease he is suffering from. Therefore the proposed work plays a vital role in avoiding the drug-drug interaction between COPD and its associated comorbidity diseases.