Browsing by Author "Soli, M.A.T."
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Item Fundus Image Classification: A Wavelet Feature Descriptor Approach(IEEE Xplore, 2023) Appati, J.K.; Armah, B.; Owusu, E.; Soli, M.A.T.Lately, many diabetic patients are experiencing diabetic retinopathy resulting in a loss of their sight. Even though the urgency and threat posed by this condition, there is insufficient data source to engage appropriate computational intelligence tools. The few that exist happen to be imbalanced. Leveraging on this imbalanced dataset, several activities have been carried out to propose improved detection and classification descriptors. Although some works have been done in this domain, the issue of accuracy still persists in the administration of an effective diagnosis. This paper harnessed the benefits of Gabor filters and the multi-resolution property of Discrete Wavelet Transforms (DWTs) to construct appropriate fundus feature descriptors. These discriminant features are fed into some selected but predominant classical machine learning classifiers. Numerical evaluation of the study gave a perfect (100%) average score for the fundus image classification using Gradient Boosting and Logistic Regression classifiers over Accuracy, F1-score, Precision and Recall evaluation metric. The tie in performance is further broken using their computation time, suggesting that Logistic Regression is more appropriate with 9min 32sec over Gradient Boosting or 1hr 10min 32sec.Item Spectral Partitioning and its Application to Image Segmentation(University of Ghana, 2015-07) Soli, M.A.T.The properties of graphs can be studied via the algebraic characteristics of its adjacency or Laplacian matrix. The second eigenvector of the graph Laplacian is one very useful tool which provides information as to how to partition a graph. In this thesis, we study spectral clustering and how to apply it in solving the image segmentation problem in computer vision.Item Validation of Performance Homogeneity of Chan-Vese Model on Selected Tumour Cells(International Journal of E-Health and Medical Communications, 2021) Appati, J.K.; Badzi, F.I.; Soli, M.A.T.; Nwolley, S.J.; Denwar, I.W.This study aims to analyze the Chan-Vese model’s performance using a variety of tumor images. The processes involve the tumors’ segmentation, detecting the tumors, identifying the segmented tumor region, and extracting the features before classification occurs. In the findings, the Chan-Vese model performed well with brain and breast tumor segmentation. The model on the skin performed poorly. The brain recorded DSC 0.6949903, Jaccard 0.532558; the time elapsed 7.389940 with an iteration of 100. The breast recorded a DSC of 0.554107, Jaccard 0.383228; the time elapsed 9.577161 with an iteration of 100. According to this study, a higher DSC does not signify a well-segmented image, as the breast had a lower DSC than the skin. The skin recorded a DSC of 0.620420, Jaccard 0.449717; the time elapsed 17.566681 with an iteration of 200.