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Detection and real-time classification of abnormal bio-cells by image segmentation technique

Abstract : Development of methods for help diagnosis of the real time detection of abnormal cells (which can be considered as cancer cells) through bio-image processing and detection are most important research directions in information science and technology. Our work has been concerned by developing automatic reading procedures of the normal and abnormal bio-images tissues. Therefore, the first step of our work is to detect a certain type of abnormal bio-images associated to many types evolution of cancer within a Microscopic multispectral image, which is an image, repeated in many wavelengths. And using a new segmentation method that reforms itself in an iterative adaptive way to localize and cover the real cell contour, using some segmentation techniques. It is based on color intensity and can be applied on sequences of objects in the image. This work presents a classification of the abnormal tissues using the Convolution neural network (CNN), where it was applied on the microscopic images segmented using the snake method, which gives a high performance result with respect to the other segmentation methods. This classification method reaches high performance values, where it reaches 100% for training and 99.168% for testing. This method was compared to different papers that uses different feature extraction, and proved its high performance with respect to other methods. As a future work, we will aim to validate our approach on a larger datasets, and to explore different CNN architectures and the optimization of the hyper-parameters, in order to increase its performance, and it will be applied to relevant medical imaging tasks including computer-aided diagnosis
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Submitted on : Tuesday, March 1, 2022 - 11:30:36 AM
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  • HAL Id : tel-01920276, version 1



Hawraa Haj-Hassan. Detection and real-time classification of abnormal bio-cells by image segmentation technique. Image Processing [eess.IV]. Université de Lorraine; Université libanaise, 2018. English. ⟨NNT : 2018LORR0043⟩. ⟨tel-01920276⟩



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