Prediction of pathological complete response to neoadjuvant chemotherapy in locally advanced breast cancer by using a deep learning model with 18F-FDG PET/CT.

OBJECTIVES: The aim of the study is 18F-FDG PET/CT imaging by using deep learning method are predictive for pathological complete response pCR after Neoadjuvant chemotherapy (NAC) in locally advanced breast cancer (LABC). INTRODUCTION: NAC is the standard treatment for locally advanced breast cancer (LABC). Pathological complete response (pCR) after NAC is considered a good predictor of disease-free survival (DFS) and overall survival (OS).Therefore, there is a need to develop methods that can predict the pCR at the time of diagnosis. METHODS: This article was designed as a retrospective chart study.For the convolutional neural network model, a total of 355 PET/CT images of 31 patients were used. All patients had primary breast surgery after completing NAC. RESULTS: Pathological complete response was obtained in a total of 9 patients. The study results show that our proposed deep convolutional neural networks model achieved a remarkable success with an accuracy of 84.79% to predict pathological complete response. CONCLUSION: It was concluded that deep learning methods can predict breast cancer treatment.

Android malware detection using hybrid ANFIS architecture with low computational cost convolutional layers.

Background: Android is the most widely used operating system all over the world. Due to its open nature, the Android operating system has become the target of malicious coders. Ensuring privacy and security is of great importance to Android users. Methods: In this study, a hybrid architecture is proposed for the detection of Android malware from the permission information of applications. The proposed architecture combines the feature extraction power of the convolutional neural network (CNN) architecture and the decision making capability of fuzzy logic. Our method extracts features from permission information with a small number of filters and convolutional layers, and also makes the feature size suitable for ANFIS input. In addition, it allows the permission information to affect the classification without being neglected. In the study, malware was obtained from two different sources and two different data sets were created. In the first dataset, Drebin was used for malware applications, and in the second dataset, CICMalDroid 2020 dataset was used for malware applications. For benign applications, the Google Play Store environment was used. Results: With the proposed method, 92% accuracy in the first data set and 92% F-score value in the weighted average was achieved. In the second data set, an accuracy of 94.6% and an F-score of 94.6% on the weighted average were achieved. The results obtained in the study show that the proposed method outperforms both classical machine learning algorithms and fuzzy logic-based studies.