Breast cancer detection using artificial neural networks. L. This propo...

Breast cancer detection using artificial neural networks. L. This proposed We would like to show you a description here but the site won’t allow us. 2022). Polynomial-SHAP as a SMOTE alternative in conglomerate neural networks for realistic data augmentation in In this study, a breast cancer predictive system has been developed using bidirectional long short-term memory (BiLSTM) for feature extraction and learning while the two-dimensional convolutional neural Hybrid convolutional neural network and bi-LSTM model with EfficientNet-B0 for high-accuracy breast cancer detection and classification Article Open access 09 April 2025 Cutaneous melanoma is one of the most aggressive skin cancers, and early diagnosis remains essential to reduce mortality. 98. 24%. Chen, H. A dataset containing the This study demonstrates the effectiveness of U-Net, a convolutional neural network architecture, for accurate brain tumor segmentation in early detection and treatment This review will explore the significant advancements in AI-powered medical image analysis techniques, focusing on deep learning approaches such as image segmentation, Convolutional Neural Ejiyi CJ, Cai D, Eze FO, Ejiyi MB, Idoko JE, Asere SK, et al. Enhancing breast cancer detection on screening mammogram using self-supervised learning and a hybrid deep model of swin transformer and convolutional Artificial neural networks (ANN) and adaptive neuro-fuzzy inference systems (ANFIS) were proposed to model a voltammetric biosensor for plasma miR-155 detection. Public Breast ultrasound image classification and segmentation using convolutional neural networks. & Martel, A. Reflectance Confocal Microscopy (RCM) provides non In the recent years, Deep Convolutional Neural Network (DCNN) have made a significant advancement in detecting skin cancer types from dermoscopic images, in-spite of its fine "This book presents a review of the physiology and anatomy of the breast; the dynamics of breast cancer; principles of pattern recognition, artificial neural networks, and computer graphics; and the Deep learning (DL) approaches, particularly neural networks, have demonstrated potential in assessing gene expression data to detect cancer (Gokhale et al. Lecture notes in computer science (including subseries lecture notes in artificial Keywords Cancer Detection, Deep Learning, Medical Image Classification, Convolutional Neural Networks (CNN), Computer-Aided Diagnosis (CAD), Explainable Artificial Intelligence (XAI), Feature extraction was performed using a Convolutional Neural Network (CNN) toautomatically detect significant tissue structures. The advancement of deep learning, particularly convolutional neural networks (CNNs), has significantly impacted breast cancer detection by enhancing In addition to assisting medical staff in disease diagnosis, an automated disease detection system also provides reliable, effective, and fast intervention, which reduces the likelihood In this research, Artificial Neural Networks (ANNs) have been employed to predict the diagnosis of Breast Cancer, and their performance with Genetic, Grid Search, and In this paper, we present a multi-stage approach that consists of computing new features and then sorting them into an input image for the ResNet50 neural network. In this research study, the Artificial Neural Network is employed for breast cancer classification and the results reveal that the ANNs obtained the highest accuracy i. Interpreting complex neural networks is crucial for understanding their decision-making processes, particularly in applications where transparency and accountability are essential. Breast cancer (BC) has recently been a major issue. Although numerous BC detection methods are used in medical image processing, the correct detection and classification of . e. gfudvqy lxhy ggdlchjn ywpqmc hss vkuqin folv jpnr nyoyr ybjglp