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Healthcare analytics leverages extensive patient data for data-driven decision-making, enhancing patient care and results. Diabetic Retinopathy (DR), a complication of diabetes, stems from damage to the retina’s blood vessels. It can affect both type 1 and type 2 diabetes patients. Ophthalmologists employ retinal images for accurate DR diagnosis and severity assessment. Early detection is crucial for preserving vision and minimizing risks. In this context, we utilized a Kaggle dataset containing patient retinal images, employing Python’s versatile tools. Our research focuses on DR detection using deep learning techniques. We used a publicly available dataset to apply our proposed neural network and transfer learning models, classifying images into five DR stages. Python libraries like TensorFlow facilitate data preprocessing, model development, and evaluation. Rigorous cross-validation and hyperparameter tuning optimized model accuracy, demonstrating their effectiveness in early risk identification, personalized healthcare recommendations, and improving patient outcomes.

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The creation of 3D models of bone from CT images has become popular for surgical planning, the design of implants, and educational purposes. Software is available to convert CT images into 3D models of bone, however, these can be expensive and technically taxing. The goal of this project was to create an open-source and easy-to-use methodology to create 3D models of bone and allow the user to interact with the model to extract desired regions. The method was first created in MATLAB and ported to Python. The CT images were imported into Python and the images were then binarized using a desired threshold determined by the user and based on Hounsfield Units (HU). A Canny edge detector was applied to the binarized images, this extracted the inner and outer surfaces of the bone. Edge points were assigned x, y, and z coordinates based on their pixel location, and the location of the slice in the stack of CT images to create a 3D point cloud. The application of a Delaunay tetrahedralization created a mesh object, the surface was extracted and saved as an STL file. An add-on in Blender was created to allow the user to select the CT images to import, set a threshold, create a 3D mesh model, draw an ROI on the model, and extract that region based on the desired thickness and create a new 3D object. The method was fully open-sourced so was inexpensive and was able to create models of a skull and allow the segmentation of portions of that mesh to create new objects. Future work needs to be conducted to improve the quality of the mesh, implement sampling to reduce the time to create the mesh, and add features that would benefit the end-user. / Thesis / Master of Applied Science (MASc) / The creation of 3D models of bone from CT images has become popular for education, surgical planning, and the design of implants. Software is available to convert CT images into 3D models but can be expensive and technically taxing. The purpose of this project was to develop a process to allow surgeons to create and interact with models from imaging data. This project applied a threshold to binarize a set of CT images, extracted the edges using a Canny Edge detector, and used the edge pixels to create a 3D point cloud. The 3D point cloud was then converted to a mesh object. A user interface was implemented that allowed the selection of portions of the model and a new 3D model to be created from the selection. The process can be improved by improving the quality of the mesh output and adding features to the user interface.

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No description available.