Personal health is a world-wide issue which poses a lot of problems especially where quality health services may be scarce and unsupervised. In order to revolutionize the system of examining CT scans of various diseases, Lung Cancer Detection System (LUCID) has been developed by Pakistani researchers.
The accuracy levels showed by this proposed systems are remarkable. The new system is taking 7 minutes per analysis whereas the conventional system takes 1 hour 30 minutes to process and diagnose that has potential commercial value.
Cancer is lethal, a disease defined to be associated with abnormality in cell growth followed by irregular cell reproduction. It is marked to be the major reason of death globally. Detection of cancer at an early stage is still a challenge for radiologist. A screening test should detect all extant cancers while avoiding unnecessary workups. Accurate and consistent diagnosis of lung cancer while looking at CT scan is difficult for the radiologists. Analysis and understanding of CT scan images as well as other medical images is quite difficult and a very time consuming procedure. The application and implementation of computer aided diagnosis using image processing and machine learning techniques can significantly make the process of diagnosis and prognosis much more effective and efficient.
The system revolves around the basic functionality of image processing and machine learning along with a maintained database where the medical records of the patients are maintained. The complex framework provides extensive medical analysis of Computed Tomography (CT) Scan images in Digital Imaging and Communications in Medicine (DICOM) format. It would facilitate radiologists in diagnosis of Lung Cancer and providing with a probability to differentiate between the types of tumour by automated analysis, with a significant increase in accuracy. The method incorporates some noise removal functions, segmentation, morphological operations and advanced image processing algorithms to extract features that serve as a baseline for prediction using machine learning. Optimization of the whole system leads to Data Analytics in order to draw conclusions about the nature of tumour with the demographic data. This computer aided diagnosis would help the Radiologists to detect tumour at any early stage, decreasing the enormous false positive rate and the overall cost of the diagnostic procedure, thus bringing windfall benefits in the field of medicine.
Cancer mortality can be reduced if cases are detected and treated early. There are 2 components of early detection:
When identified early, cancer is more likely to respond to effective treatment and can result in a greater probability of surviving, less morbidity, and less expensive treatment. Significant improvements can be made in the lives of cancer patients by detecting cancer early and avoiding delays in care.
Early diagnosis consists of 3 steps that must be integrated and provided in a timely manner:
Early diagnosis is relevant in all settings and the majority of cancers. In absence of early diagnosis, patients are diagnosed at late stages when curative treatment may no longer be an option. Programmes can be designed to reduce delays in, and barriers to, care, allowing patients to access treatment in a timely manner.
Screening aims to identify individuals with abnormalities suggestive of a specific cancer or pre-cancer who have not developed any symptoms and refer them promptly for diagnosis and treatment.
Screening programmes can be effective for select cancer types when appropriate tests are used, implemented effectively, linked to other steps in the screening process and when quality is assured. In general, a screening programme is a far more complex public health intervention compared to early diagnosis.