<span>Tracking drugs became more difficult using the centralized architecture. Decentralized architecture using blockchain technology overcomes the difficulties faced by the centralized network like availability and recovery. Avoiding duplicate or fake drugs created by fake manufacturers is a big challenge in the centralized network. Authentic stock is managed and the supply chain is tracked efficiently using this blockchain technology. This is addressed by using the smart contract which helps to track the movement of drugs from manufacturer to supplier, supplier to the reseller, reseller to pharmacies and finally pharmacies to patient. By default, duplicate drugs or fake drugs are completely avoided by using the blockchain technology. Patients buy drugs without any prescription and it creates a lot of problems in real life. So, Patients cannot buy drugs without authenticated doctor’s prescriptions with the help of a QR Code scanner attached with the prescription which will be implemented using a mobile application and cannot buy excess drugs which might lose someone’s life. With the help of inventory management, the maximum limit of drugs to avoid overdose and pharmacies cannot sell those overdose drugs. Consulting a doctor before buying a drug for even a simple illness is important and it is tracked by using prescriptions provided by authentic doctors. In this project, these challenges are addressed using the smart contract which is written in solidity language and runs on a public ethereum network.</span>

Cancer has become very common in this evolving world. Technology advancements, increased radiations have made cancer a common syndrome. Various types of cancers like Skin Cancer, Breast Cancer, Prostate Cancer, Blood Cancer, Colorectal cancer, Kidney Cancer and Lung Cancer exits. Among these various types of cancers, the mortality rate is high in lung cancer which is tough to diagnose and can be diagnosed only in advanced stages. Small cell lung cancer and non-small cell lung cancer are the two types in which non-small cell lung cancer (NSCLC) is the most common type which makes up to 80 to 85 percent of all cases [1]. Digital Image Processing and Artificial Intelligence advancements has helped a lot in medical image analysis and Computer Aided Diagnosis(CAD). Numerous research is carried out in this field to improve the detection and prediction of the cancerous tissues. In current methods, traditional image processing techniques is applied for image processing, noise removal and feature extraction. There are few good approaches that applies Artificial Intelligence and produce better results. However, no research has achieved 100% accuracy in nodule detection, early detection of cancerous nodules nor faster processing methods. Application of Artificial Intelligence techniques like Machine Learning, Deep Learning is very minimal and limited. In this paper [Figure 1], we have applied Artificial intelligence techniques to process CT (Computed Tomography) Scan image for data collection and data model training. The DICOM image data is saved as numpy file with all medical information extracted from the files for training. With the trained data we apply deep learning for noise removal and feature extraction. We can process huge volume of medical images for data collection, image processing, detection and prediction of nodules. The patient is made well aware of the disease and enabled with their health tracking using various mobile applications made available in the online stores for iOS and Android mobile devices.