Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  a computational alternative to classify lung nodules inside CT thorax images in the frequency domain is presented. After image acquisition, a region of interest is manually selected. Then, the spectrums of the two dimensional Discrete Cosine Transform (2D-DCT) and the two dimensional Fast Fourier Transform (2D-FFT) re calculated. Later, two statistical texture features re extracted from the histogram computed from the spectrum of each CT image. Finally, a support vector machine with a radial basis function as a kernel was used as the classifier. Seventy five tests with different diagnosis and number of images re used to validate the methodology presented. After experimentation and results, ten false negatives (FN) and two false positives (FP) re obtained, and a sensitivity and specificity of 96.15% and 52.17% respectively. The total preciseness obtained with the methodology proposed was 82.66%.
['Hiram Madero Orozco', 'Osslan Osiris Vergara Villegas', 'Leticia Ortega Maynez', 'Vianey Guadalupe Cruz Sánchez', 'Humberto de Jesús Ochoa Domínguez']
I am writing a research paper on a idea Lung nodule classification in frequency domain using support vector machines
[{"from": "human", "value": "I am writing a research paper on a idea Lung nodule classification in frequency domain using support vector machines"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  a computational alternative to classify lung nodules inside CT thorax images in the frequency domain is presented. After image acquisition, a region of interest is manually selected. Then, the spectrums of the two dimensional Discrete Cosine Transform (2D-DCT) and the two dimensional Fast Fourier Transform (2D-FFT) re calculated. Later, two statistical texture features re extracted from the histogram computed from the spectrum of each CT image. Finally, a support vector machine with a radial basis function as a kernel was used as the classifier. Seventy five tests with different diagnosis and number of images re used to validate the methodology presented. After experimentation and results, ten false negatives (FN) and two false positives (FP) re obtained, and a sensitivity and specificity of 96.15% and 52.17% respectively. The total preciseness obtained with the methodology proposed was 82.66%."}]