We report a multi-class classification model built using random forest (RF) and synthetic minority oversampling technique (SMOTE) applied to extracted intrinsic fluorescence (IF) data to detect normal, pre-cancer, and cancer samples. Important features in the fluorescence signal often get suppressed by the noise which makes denoising an essential pre-processing step. The proposed algorithm implements a wavelet-based denoising technique as a pre-processing step before data analysis which utilizes the “coif3” mother wavelet function to denoise IF data. Synthetic minority oversampling technique (SMOTE) is utilized to generate a balanced dataset. We achieved the best classification for the denoised balanced dataset with accuracy, sensitivity, and specificity above 90% for normal/pre-cancer and precancer/cancer groups. Further, the receiver operating curve (ROC) shows a clear distinction among three grades with the area under curve (AUC) of 0.96 for normal and precancer samples and 1.00 for cancer samples. The python script prepared for this study is available on GitHub and Signal Science Lab.
We demonstrate a Bayesian statistics-based outlier separation algorithm, which clearly distinguishes microscope captured images of unstained human cervical tissue sections of normal and different grades of precancerous tissues. The semi-automated global and adaptive method implements outlier separation based on the statistical characterization of the image histogram distribution. This multi-level thresholding achieves an effective image quantization of the high cell density domain, most affected in the progression of the disease, which yields a precise visualization of the lesions in the epithelium cellular structures, revealing their temporal changes with the progression of the disease. The pixel count ratio of the quantized high cell density region, below a statistically well-defined threshold, quantitatively discriminates different grades of precancer tissues through Receiver Operating Characteristics.
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