Intelligent Detection System for diagnosis of Systemic Lupus Erythematosus (SLE)

SLE is a disease that is incurable. However, due to the limited technology, some patients has missed the treatment as they aren’t been tested to have the positive test having lupus until the disease is uncontrollable and spread through their body and causing death. Therefore, in this paper, we proposed an intelligent detection system for the SLE to have the more accurate diagnostic solution for the patients. In this intelligent detection system, MATLAB is approached and the 4 stages, pre-processing ANA image for the Human Epithelial type 2 (HEP-2) cells, segmentation, feature extraction, and classification are being done to have the accurate detection of the SLE disease. After reading some literature reviews for the research done previously by a lot of researchers, especially Paola Soda, the most appropriate techniques has been applied for this intelligent detection system of SLE. In conclusion, the intelligent detection of SLE by MATLAB approach is going to be a simple and effective ways to help in the medical field for the more accurate results in diagnosis of lupus

Automated analysis of colorectal cancer

Colorectal cancer (CRC) is the second largest cause of cancer deaths in the UK, with approximately 16,000 per year. Over 41,000 people are diagnosed annually, and 43% of those will die within ten years of diagnosis. The treatment of CRC patients relies on pathological examination of the disease to identify visual features that predict growth and spread, and response to chemoradiotherapy. These prognostic features are identified manually, and are subject to inter and intra-scorer variability. This variability stems from the subjectivity in interpreting large images which can have very varied appearances, as well as the time consuming and laborious methodology of visually inspecting cancer cells. The work in this thesis presents a systematic approach to developing a solution to address this problem for one such prognostic indicator, the Tumour:Stroma Ratio (TSR). The steps taken are presented sequentially through the chapters, in order of the work carried out. These specifically involve the acquisition and assessment of a dataset of 2.4 million expert-classified images of CRC, and multiple iterations of algorithm development, to automate the process of generating TSRs for patient cases. The algorithm improvements are made using conclusions from observer studies, conducted on a psychophysics experiment platform developed as part of this work, and further work is undertaken to identify issues of image quality that affect automated solutions. The developed algorithm is then applied to a clinical trial dataset with survival data, meaning that the algorithm is validated against two separate pathologist-scored, clinical trial datasets, as well as being able to test its suitability for generating independent prognostic markers