Photodiagnosis and photodynamic recognition of cervical cancer with SEM and AFM images - Aytac, Sevcan; Ozbey, Gurkan (Public Library Science, 2025)
So far, the number of patients who die from cancer is quite high. Continuation of early detection research is important to reduce the number of deaths due to cancer. At the time of the literature review, images of the same patients taken from Scanning Electron Microscope (SEM) and Atomic Force Microscope (AFM) for early diagnosis of cervix cancer have not been addressed to date. This article, Photodiagnosis and Photodynamics with SEM and AFM images are valuable in recognizing cervical cancer and starting treatment early. Simultaneous examination of the, Photodiagnosis and Photodynamics with SEM and AFM cervix images of patients will provide us with a far more powerful solution than a one-way solution. Daubechies (db2, db3, db4, and db5), Coiflet (coif5, coif4, coif3, and coif2), Symlet (sym5, sym4, sym3, and sym2), and Biorthogonal (bior1.3, bior2.8, bior1.5, and bior3.3) 16 discrete wavelet transformation families (DWTF) have been applied to AFM and SEM images. One approximate and three detail coefficients have been obtained for each one AFM and SEM cervix images. Homogeneity, contrast, angular second moment, entropy, mean, standard deviation, correlation, cluster prominence, dissimilarity, and cluster shade values have been calculated for each of these one approximate and three detail coefficients. The classification rate found by the averages of the results obtained from the DWTF_JSD, DWTF_HD and DWTF_TD algorithms for AFM and SEM cervix images are 98.29% and 97.10%, respectively. According to these results, it has been determined that SEM images have lower classification rate than AFM images. It has been also observed that the surface roughness of the mAFM images was larger than nAFM and bAFM images. But, it was observed that the volume of particles of the mAFM images has been smaller than nAFM and bAFM images.
Keywords: Discrete Wavelet; Neural-Networks; Breast-Cancer; Diagnosis; Textures