Identification of microscopy cell images by using convolutional neural network application / Norfatin Farisya Ajis
Breast cancer has been the major factor of cancer death and the second main cause of women’s deaths in the world. The false positive results of this cancer cell detection during the screening test leads to false treatment and emotional disturbance of the patients. Thus, breast cancer cell lines (MCF...
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| Format: | Thesis |
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2020
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| Online Access: | http://studentsrepo.um.edu.my/11912/1/Norfatin_Farisya_binti_Ajis.jpg http://studentsrepo.um.edu.my/11912/8/farisya.pdf http://studentsrepo.um.edu.my/11912/ |
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| Summary: | Breast cancer has been the major factor of cancer death and the second main cause of women’s deaths in the world. The false positive results of this cancer cell detection during the screening test leads to false treatment and emotional disturbance of the patients. Thus, breast cancer cell lines (MCF7) is used as the microscopy image samples together with the Human Bone Osteosarcoma Epithelial Cells (U2OS), and Human Hepatocyte as control to study the effectiveness of convolutional neural network (CNN) as a method of image recognition. The objectives of this study are to determine the ability of convolutional neural network in the classification of MCF7, U2OS, and human hepatocyte and also to compare the accuracy of convolutional neural network in the detection of microscopic cells by using Resnet-50 architecture model and self-training. The first procedure is a normal CNN training without using any CNN architecture model and later added with Resnet-50 model as a transfer learning to compare the results of efficiency for both method. Both results found that it can detect the image samples according to the cell type but the training accuracy gives different percentage, where the image training accuracy for Resnet-50 gives 100% accuracy result and 79.57% accuracy for self-training. Increasing the number of image samples and image augmentation has been done to improve the result of self-training but still, both methods are not comparable because the self-training is not completely done under certain circumstances. This is a small scale work where the findings may not contribute much to the medical research, but more to exposure of machine learning application. |
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