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Classification and detection of cell based morphology
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Every living species has cells and based on those cells scientists observe and make some predictions or observations. The identification and classification of cells is a highly crucial part of medical research and involves human efforts to a huge extent. Due to human involvement, the research may go wrong and may predict inaccurate results. This time-consuming process can be eliminated if machine learning algorithms is applied. This research aims to train an ML algorithm to detect and classify cells based on a number of branches. This research will take images of a group of cells as input to the algorithm for training purposes. The training of the model will be accomplished based on the labeled images of cells. The trained model will identify and classify the cells images in three different classes Class A, Class B, and Class C. Class A cells will not have any branches, Class B will have fewer branches and Class C will include cells with many branches. To eliminate the inaccurate results. The training will be done by using Machine Learning algorithm which is used for classification problems. The training part will involve extracting features of cell from the image of the dataset. As a result of this research, medical practitioners will get the classified images of cells and they will be able to analyze the result in an organized manner. The trained model will be helpful to provide better and more accurate results. This will fasten the process of analyzing medical reports. This model can be enhanced to classify the cells and also analyze the classification report to provide better predictions regarding the cells.
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- Date created
- 2022
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- Type of Item
- Research Material