Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Malignant breast tumors and benign masses appear in mammograms with different shape characteristics: the former usually have rough, spiculated, or microlobulated contours, whereas the latter commonly have smooth, round, oval, or macrolobulated contours. Visual features that characterize shape roughness can assist in distinguishing beten malignant tumors and benign masses in mammo-grams. Here  propose a new approach based on Hilbert curves to classify breast masses as benign or malignant. The feature extraction is performed in linear time, and is amenable to parallel processing, whereas the classification phase can be performed by a classical neural networks structure.  evaluated our method using a set of 111 contours from 65 benign masses and 46 malignant tumors. As the experimental evaluations show,  achieved an accuracy of 0.99 in terms of the area under the receiver operating characteristics curve, a very high classification result.
['Denise Guliato', 'Walter Amorim de Oliveira', 'Caetano Traina']
I am writing a research paper on a idea A new feature descriptor derived from Hilbert space-filling curve to assist breast cancer classification
[{"from": "human", "value": "I am writing a research paper on a idea A new feature descriptor derived from Hilbert space-filling curve to assist breast cancer classification"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Malignant breast tumors and benign masses appear in mammograms with different shape characteristics: the former usually have rough, spiculated, or microlobulated contours, whereas the latter commonly have smooth, round, oval, or macrolobulated contours. Visual features that characterize shape roughness can assist in distinguishing beten malignant tumors and benign masses in mammo-grams. Here  propose a new approach based on Hilbert curves to classify breast masses as benign or malignant. The feature extraction is performed in linear time, and is amenable to parallel processing, whereas the classification phase can be performed by a classical neural networks structure.  evaluated our method using a set of 111 contours from 65 benign masses and 46 malignant tumors. As the experimental evaluations show,  achieved an accuracy of 0.99 in terms of the area under the receiver operating characteristics curve, a very high classification result."}]