Background: We directly compared the mammography image processing results obtained with the help of the YOLOv4 convolutional neural network (CNN) model versus those obtained with the help of the NCA-based nested contours algorithm model. Method: We used 1080 images to train the YOLOv4, plus 100 images with proven breast cancer (BC) and 100 images with proven absence of BC to test both models. Results: the rates of true-positive, false-positive and false-negative outcomes were 60, 10 and 40, respectively, for YOLOv4, and 93, 63 and 7, respectively, for NCA. The sensitivities for the YOLOv4 and the NCA were comparable to each other for star-like lesions, masses with unclear borders, round- or oval-shaped masses with clear borders and partly visualized masses. On the contrary, the NCA was superior to the YOLOv4 in the case of asymmetric density and of changes invisible on the dense parenchyma background. Radiologists changed their earlier decisions in six cases per 100 for NCA. YOLOv4 outputs did not influence the radiologists’ decisions. Conclusions: in our set, NCA clinically significantly surpasses YOLOv4.

This letter considers a new method for sample detection of ionospheric propagation modes at the receiver output of a chirp ionosonde. The method is based on the use of the hazard function. Within the framework of the two-component mixture model, it is shown that the hazard function can be used to detect samples of the second component in the mixture when the corresponding fraction of samples in the total number is small. The implementation of the method is carried out using training sets. An average normalized hazard function is constructed for the training set not containing the signal. Normalization is carried out in such a way that the hazard function can be considered as a probability density function of a certain random variable. We used Pearson's statistic to test the agreement between the average normalized hazard function of samples that do not contain the signal and an arbitrary normalized hazard function. The critical value of the statistic was determined using the Neyman-Pearson criterion. The effectiveness of the proposed method has been tested for the determination of the lowest and maximum observed frequencies of radio paths from oblique ionospheric sounding ionograms obtained in the Russian network of circumpolar radio paths. The method suggested in this letter can be used not only when processing ionograms but also for the detection of samples of signals of arbitrary nature against a background noise, provided that statistical signal-to-noise ratios are similar to those considered here.

The amount of ultrasound (US) breast exams continues to grow because of the wider endorsement of breast cancer screening programs. When a solid lesion is found during the US the primary task is to decide if it requires a biopsy. Therefore, our goal was to develop a noninvasive US grayscale image analysis for benign and malignant solid breast lesion differentiation. We used a dataset consisting of 105 ultrasound images with 50 benign and 55 malignant non-cystic lesions. Features were extracted from the source image, the image of the gradient module after applying the Sobel filter, and the image after the Laplace filter. Subsequently, eight gray-level co-occurrence matrices (GLCM) were constructed for each lesion, and 13 Haralick textural features were calculated for each GLCM. Additionally, we computed the differences in feature values at different spatial shifts and the differences in feature values between the inner and outer areas of the lesion. The LASSO method was employed to determine the most significant features for classification. Finally, the lesion classification was carried out by various methods. The use of LASSO regression for feature selection enabled us to identify the most significant features for classification. Out of the 13 features selected by the LASSO method, four described the perilesional tissue, two represented the inner area of the lesion and five described the image of the gradient module. The final model achieved a sensitivity of 98%, specificity of 96%, and accuracy of 97%. Considering the perilesional area, Haralick feature differences, and the image of the gradient module can provide crucial parameters for accurate classification of US images. Features with a low AUC index (less than 0.6 in our case) can also be important for improving the quality of classification.