Browsing by Author "Arif, Fatima"

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Benchmarking Coefficients for Forecasting Weight Loss After Sleeve Gastrectomy Biomedical Engineering
(World Scientific Publ Co Pte Ltd, 2020) Celik, Sebahattin; Sohail, Ayesha; Arif, Fatima; Ozdemir, Abdulselam
Background/Aim: In treatment practice of obesity, losing excess weight and then maintaining an ideal body weight are very important. By the sleeve gastrectomy initial weight loss is easier, but the progress of patients have diverse variability in terms of maintaining weight loss. Predicting models for weight changes may provide doctors and patients a good tool to modify their approach to obesity treatment.The main objective of this research is to verify the dependence of weight loss on sleeve coefficients and to forecast the weight loss. The weight loss and its dependence on remnant gastric volume compartmants (antral and body parts), after laparoscopic sleeve gastrectomy (LSG) is discussed in this paper. Data was obtained from a previous study which included 63 patients. Deep analysis of weight loss after LSG and its relation with remnant gastric volume is still a challenge due to weight loss dependence on multiple factors. During this research, with the aid of machine learning regression classifier, the relationship(s) between the sleeve coefficients' formulae and weight loss formulae (%EWL and %TWL), are developed in a novel way. Other factors such as age and gender are also taken into account. A robust approach of artificial intelligence, i.e. the "Neural Network Bayesian Regularization" is adopted to utilize the third month, sixth month and first year weight loss data, to forecast the second year weight loss. Models are proposed to demonstrate the dependance of total weight loss on crucial parameters of components of remanat gastric volumes. A comparative study is conducted for the appropriate selection of artificial intelligence training algorithm.
Neural Networks To Understand the Physics of Oncological Medical Imaging
(World Scientific Publ Co Pte Ltd, 2022) Al-Utaibi, Khaled A.; Sohail, Ayesha; Arif, Fatima; Celik, S.; Sait, Sadiq M.; Keskin, Derya Bako
The evolving field of computational image analysis has its applications in the industry, manufacturing and biological sciences, especially in the field of medical imaging. Medical imaging and computational physics have evolved together during the past decades with the advancement in the field of artificial intelligence (AI). Deep learning is the sub-domain of AI that mostly deals with imaging data for classification, segmentation and reconstruction. The time series of medical images of different patients, with different staging are categorized based on the physical and biological consequences. The hypothesis of the current research is that the deep learning tool, if trained on several patients, can identify the stage of cancer swiftly for fresh data sets. During this research, an advance Convolutional Neural Network (CNN) strategy is adopted to classify the cancer stage for a group of patients of gastric cancer. The CNN model makes use of skipping connections for better prediction. CNNs have been quite popular in medical imaging for their ability of feature detection. CNNs are used in the recent literature for the analysis of images. During this research, we have used the state-of-the-art Matlab ResNet CNN toolbox for the analysis of the images obtained from esophageal and gastric cancer patients. It was concluded that RESNET50 is a reliable algorithm for the determination of tumor mass on CT Scans. Moreover, the performance of the model can be improved by giving a comparatively larger data set as an input to the model. Inspired from Caltech101, a logic related to RESNET50 was adopted. The data was processed and an algorithm was designed to develop a mapping, based on the mass of tumor. The algorithm designed successfully identified the images, randomly picked from different patients, based on the image features.