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| dc.contributor.author | Agere, Birhanu | |
| dc.date.accessioned | 2022-03-24T06:57:42Z | |
| dc.date.available | 2022-03-24T06:57:42Z | |
| dc.date.issued | 2021-10 | |
| dc.identifier.uri | http://ir.bdu.edu.et/handle/123456789/13250 | |
| dc.description.abstract | The major challenge in liver cancer detection is detecting hepatocellular carcinoma (HCC). HCC is one of the most common types of primary malignant liver cancer, and it has become a leading cause of cancer death in recent years. Primary liver cancer may begin as a single lump growing in the liver, or it may begin in multiple locations throughout the liver at the same time. Early detection of HCC helps in controlling the disease progression. Now a day a radiologist detect HCC by using an abdomen CT image manually. The lack of experienced CT radiologists and simple diagnostic techniques concerning the number of patients delays the diagnosis and treatments of the patients. There are different researches conducted to detect HCC using machine learning but still, there is a limitation in the techniques they used for preprocessing and feature extraction techniques. To reduce this limitation an alternative HCC and cirrhosis detection model is developed. To design the model abdominal CT scan images with DICOM file format from CASMA radiology center, Felege Hiwot referral hospital, and Tibebe Gion specialized hospital in BahirDar city with size 512x512 was collected and converted to JPEG, all of which are labeled by radiologist. To achieve the objective of the study, the Python programming language was used to implement the model on the top of the Tensor flow and Keras API. To enhance the processing time and accuracy of the detection model preprocessing techniques such as resizing by Bicubic interpolation technique, noise reduction by BM3D filter, and contrast enhancement by contrast limited adaptive histogram equalization was applied. After getting an enhanced image extracting of representing features using gray level co-occurrence matrix (GLCM) and convolutional neural network (CNN) took place. Besides, four convolutional layers and five GLCM features were used. Finally, the extracted features were fed to the KNN classifier both independently and in the combined approach. The model achieved 83.18%, 98.34% using GLCM and CNN features respectively and combined features achieves 99.26% accuracy. Keywords: HCC, Cirrhosis detection, CNN, GLCM, KNN | en_US |
| dc.language.iso | en_US | en_US |
| dc.subject | Communication System Engineering | en_US |
| dc.title | Developing hepatocellular carcinoma and cirrhosis detection model on CT images using computer vision approach | en_US |
| dc.type | Thesis | en_US |
