Lung Cancer Classification and Prediction Using Machine Learning and Image Processing.

Lung cancer is a potentially lethal illness. Cancer detection continues to be a challenge for medical professionals. The true cause of cancer and its complete treatment have still not been discovered. Cancer that is caught early enough can be treated. Image processing methods such as noise reduction, feature extraction, identification of damaged regions, and maybe a comparison with data on the medical history of lung cancer are used to locate portions of the lung that have been impacted by cancer. This research shows an accurate classification and prediction of lung cancer using technology that is enabled by machine learning and image processing. To begin, photos need to be gathered. In the experimental investigation, 83 CT scans from 70 distinct patients were utilized as the dataset. The geometric mean filter is used during picture preprocessing. As a consequence, image quality is enhanced. The K-means technique is then used to segment the images. The part of the image may be found using this segmentation. Then, classification methods using machine learning are used. For the classification, ANN, KNN, and RF are some of the machine learning techniques that were used. It is found that the ANN model is producing more accurate results for predicting lung cancer.

Non-contact ulcer area calculation system for neuropathic foot ulcer.

BACKGROUND: Around 125,785 new cases in year 2013-14 of leprosy were detected in India as per WHO report on leprosy in September 2015 which accounts to approximately 62% of the total new cases. Anaesthetic foot caused by leprosy leads to uneven loading of foot leading to ulcer in approximately 20% of the cases. Much efforts have gone in identifying newer techniques to efficiently monitor the progress of ulcer healing. Current techniques followed in measuring the size of ulcers, have not been found to be so accurate but are still is followed by clinicians across the globe. Quantification of prognosis of the condition would be required to understand the efficacy of current treatment methods and plan for further treatment. This study aims at developing a non contact technique to precisely measure the size of ulcer in patients affected by leprosy. METHODS: Using MATLAB software, GUI was designed to process the acquired ulcer image by segmenting and calculating the pixel area of the image. The image was further converted to a standard measurement using a reference object. The developed technique was tested on 16 ulcer images acquired from 10 leprosy patients with plantar ulcers. Statistical analysis was done using MedCalc analysis software to find the reliability of the system. RESULTS: The analysis showed a very high correlation coefficient (r=0.9882) between the ulcer area measurements done using traditional technique and the newly developed technique, The reliability of the newly developed technique was significant with a significance level of 99.9%. CONCLUSIONS: The designed non-contact ulcer area calculating system using MATLAB is found to be a reliable system in calculating the size of ulcers. The technique would help clinicians have a reliable tool to monitor the progress of ulcer healing and help modify the treatment protocol if needed.

Design of mechanical interface to re-distribute excess pressure to prevent the formation of decubitus ulcers in bed ridden patients.

Pressure ulcers are the major problem in the stroke management and rehabilitation. Prevention of pressure ulcer is of keen interest and is achieved by frequently changing the position of patient on the mattress. However, the care needs to be intensive to address this issue; else it would lead to pressure ulcer or bed sores formation. Skin surface over the bony prominences provide comparatively more pressure than the other regions. Therefore they are called as pressure vulnerable regions. Skin over these regions is more susceptible for formation of ulcers. An engineering approach is needed to shift the accumulating pressure from the pressure vulnerable regions. Although pressure sensed in these region would be more than that of which sensed in other regions, shifting protocol has to be designed to channelize or to grade the pressure shift in order to avoid any injuries to the non pressure vulnerable region. This paper aims at devising one such protocol using MATLab and thereby designing the layout of mattress using Pro/Engineer: the number of partitions needed to cover the entire surface of the skin that is in contact with the mattress.