Detection of Lung Cancer and Treatment Suggestion based on the Severity of the cancer

In order to improve healthcare management, it is important to track health outcomes. For medical researchers, machine learning techniques have become a popular means of making accurate predictions. Machine learning techniques can identify trends in large amounts of dataset, and capable of predicting cancer accurately. In the current medical research, medical imaging plays a significant role in thorough examination and diagnosis of the entire human body. Medical professionals rely entirely on computed tomography results acquired from the image sensors. Lungs are the most primary organ of the human respiratory system. Medical professionals face a challenging task in accurately predicting the lung cancer. Detecting the lung cancer aids in determining the appropriate treatment, which increases the chances of survival for the lung cancer patient. In this project the lung cancer dataset is taken as input, the cancerous images are divided into five clusters based on the features obtained using K-Means clustering. Now, detection of cancer cells can be done by using transfer learning. In this project Densenet121 model in transfer learning is used to identify whether the cancer cells are present or not. If the CT scan image is cancerous then the treatment associated with the type is suggested to the user. This would help the physician to guide the patient on whether to take surgery or to take other kind of treatments. This is also used for Insurance companies. In this project, Image processing, transfer learning, and K-means clustering are used to cluster the cancer images based on features and to identify whether the image is cancerous or not and to suggest treatment.