Cyclic GAN Model to Classify Breast Cancer Data for Pathological Healthcare Task

Abstract

An algorithm framework based on CycleGAN and an upgraded dual-path network (DPN) is suggested to address the difficulties of uneven staining in pathological pictures and difficulty of discriminating benign from malignant cells. CycleGAN is used for color normalization in pathological pictures to tackle the problem of uneven staining. However, the resultant detection model is ineffective. By overlapping the images, the DPN uses the addition of small convolution, deconvolution, and attention mechanisms to enhance the model’s ability to classify the texture features of pathological images on the BreaKHis dataset. The parameters that are taken into consideration for measuring the accuracy of the proposed model are false-positive rate, false-negative rate, recall, precision, and F 1 score. Several experiments are carried out over the selected parameters, such as making comparisons between benign and malignant classification accuracy under different normalization methods, comparison of accuracy of image level and patient level using different CNN models, correlating the correctness of DPN68-A network with different deep learning models and other classification algorithms at all magnifications. The results thus obtained have proved that the proposed model DPN68-A network can effectively classify the benign and malignant breast cancer pathological images at various magnifications. The proposed model also is able to better assist the pathologists in diagnosing the patients by synthesizing the images of different magnifications in the clinical stage.

Effectiveness of a diagnostic algorithm for dengue based on an artificial neural network

“…Likewise, another strength apart from having a standardized criterion for the study of people with suspected dengue is that the cases were included directly from the epidemiological surveillance system after validation of the operational personnel responsible for epidemiological surveillance. Although there are other algorithms that have been used in medical diagnosis with a good level of accuracy when compared with traditional machine learning and volumetric techniques 26 ; all of them have focused on images (radiological 27 or histopathological 28 ); based on Convolutional Neural Network 26 or dual-path network. 28 In the present study, data from various sources of information (clinical records and epidemiological surveillance systems) were used, which is why an ANN was used in the study.…”

“…Although there are other algorithms that have been used in medical diagnosis with a good level of accuracy when compared with traditional machine learning and volumetric techniques 26 ; all of them have focused on images (radiological 27 or histopathological 28 ); based on Convolutional Neural Network 26 or dual-path network. 28 In the present study, data from various sources of information (clinical records and epidemiological surveillance systems) were used, which is why an ANN was used in the study. The epidemiological arbovirosis surveillance (included dengue, zika, and chikungunya) in Mexico is based on the reporting of probable and/or laboratory-confirmed cases to the National Epidemiological Surveillance System, with a subsample of 30% sent for laboratory confirmation, 29 that although it operates throughout the country in a homogeneous manner, in the present study it was contemplated in a medical unit; Therefore, the limitations of the study were (1) the number of records involved from a secondary-care hospital, (2) not including or stratifying by adjacent diseases, (3) the entire year was included without delineating the epidemic periods, (4) dengue fever was not differentiated by severity and (5) participant were not matched by sex.…”

Exploring Deepfakes - Creation Techniques, Detection Strategies, and Emerging Challenges: A Survey

“…These layers captured latent facial features such as eye movements using an autoencoder-decoder approach, thereby enhancing the quality and authenticity of generated synthetic images. [9] CycleGAN is a deepfake method that uses the GAN architecture to extract distinctive features from one image and apply them to another image. This method makes use of a cycle loss function to speed up the discovery of latent features.…”

U-Net-Based Models towards Optimal MR Brain Image Segmentation

“…After that, a voting process (or maybe a more sophisticated method such as Bayesian weighting) is applied to select the correct label from the labels space and assign it to the segments. The new scheme of deep learning as a main category of artificial intelligence is used for a variety of medical applications such as monitoring neurological disorder patients [1], pneumonia classification using chest X-ray [2], brain tumor classification [3], breast cancer [4][5][6], COVID-19 detection from chest CT scans [7,8], and other non-medical applications [9] such as hate speech prediction [10][11][12] The deep learning approach for medical imaging segmentation includes the multi-level features representation from image intensities. The ability to learn features representation through non-linear transformation is a plus point for deep learning compared to machine learning algorithms, where there is less dependency on the prior knowledge of the field of application.…”