Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The development of an automated algorithm for the categorization of normal and cancerous colon mucosa is reported. Six features based on texture analysis re studied. They re derived using the co-occurrence matrix and re angular second moment, entropy, contrast, inverse difference moment, dissimilarity, and correlation. Optical density was also studied. Forty-four normal images and 58 cancerous images from sections of the colon re analyzed. These two groups re split equally into two subgroups: one set was used for supervised training and the other to test the classification algorithm. A stepwise selection procedure shod that correlation and entropy re the features that discriminated most strongly beten normal and cancerous tissue (P<0.0001). A parametric linear-discriminate function was used to determine the classification rule. For the training set, a sensitivity and specificity of 93.1% and 81.8%, respectively, re achieved, with an overall accuracy of 88.2%. These results mere confirmed with the test set, with a sensitivity and specificity of 93.1% and 86.4%, respectively, and an overall accuracy of 90.2%.
['A. Nasser Esgiar', 'R.N.G. Naguib', 'Bayan S. Sharif', 'Mark K. Bennett', 'A. Murray']
I am writing a research paper on a idea Microscopic image analysis for quantitative measurement and feature identification of normal and cancerous colonic mucosa
[{"from": "human", "value": "I am writing a research paper on a idea Microscopic image analysis for quantitative measurement and feature identification of normal and cancerous colonic mucosa"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The development of an automated algorithm for the categorization of normal and cancerous colon mucosa is reported. Six features based on texture analysis re studied. They re derived using the co-occurrence matrix and re angular second moment, entropy, contrast, inverse difference moment, dissimilarity, and correlation. Optical density was also studied. Forty-four normal images and 58 cancerous images from sections of the colon re analyzed. These two groups re split equally into two subgroups: one set was used for supervised training and the other to test the classification algorithm. A stepwise selection procedure shod that correlation and entropy re the features that discriminated most strongly beten normal and cancerous tissue (P<0.0001). A parametric linear-discriminate function was used to determine the classification rule. For the training set, a sensitivity and specificity of 93.1% and 81.8%, respectively, re achieved, with an overall accuracy of 88.2%. These results mere confirmed with the test set, with a sensitivity and specificity of 93.1% and 86.4%, respectively, and an overall accuracy of 90.2%."}]