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Breast cancer (BC) is a type of tumor that develops in the breast cells and is one of the most common cancers in women. Women are also at risk from BC, the second most life-threatening disease after lung cancer. The early diagnosis and classification of BC are very important. Furthermore, manual detection is time-consuming, laborious work, and, possibility of pathologist errors, and incorrect classification. To address the above highlighted issues, this paper presents a hybrid deep learning (CNN-GRU) model for the automatic detection of BC-IDC (+,−) using whole slide images (WSIs) of the well-known PCam Kaggle dataset. In this research, the proposed model used different layers of architectures of CNNs and GRU to detect breast IDC (+,−) cancer. The validation tests for quantitative results were carried out using each performance measure (accuracy (Acc), precision (Prec), sensitivity (Sens), specificity (Spec), AUC and F1-Score. The proposed model shows the best performance measures (accuracy 86.21%, precision 85.50%, sensitivity 85.60%, specificity 84.71%, F1-score 88%, while AUC 0.89 which overcomes the pathologist’s error and miss classification problem. Additionally, the efficiency of the proposed hybrid model was tested and compared with CNN-BiLSTM, CNN-LSTM, and current machine learning and deep learning (ML/DL) models, which indicated that the proposed hybrid model is more robust than recent ML/DL approaches.

E-health has grown into a billion-dollar industry in the last decade. Its device’s high throughput makes it an obvious target for cyberattacks, and these environments desperately need protection. In this scientific study, we presented an artificial intelligence (AI)-driven software-defined networking (SDN)-enabled intrusion detection system (IDS) to address increasing cyber threats in the E-health and internet of medical things (IoMT) environments. AI’s success in various fields, including big data and intrusion detection systems, has prompted us to develop a flexible and cost-effective approach to protect such critical environments from cyberattacks. We present a hybrid model consisting of long short-term memory (LSTM) and gated recurrent unit (GRU). The proposed model was thoroughly evaluated using the publicly available CICDDoS2019 dataset and conventional evaluation measures. Furthermore, for proper validation, the proposed framework is compared with relevant classifiers, such as cu-GRU+ DNN and cu-BLSTM. We have further compared the proposed model with existing literature to prove its efficacy. Lastly, 10-fold cross-validation is also used to verify that our results are unbiased. The proposed approach has bypassed the current literature with extraordinary performance ramifications such as 99.01% accuracy, 99.04% precision, 98.80 percent recall, and 99.12% F1-score.

Brain tumors (BTs) are spreading very rapidly across the world. Every year, thousands of people die due to deadly brain tumors. Therefore, accurate detection and classification are essential in the treatment of brain tumors. Numerous research techniques have been introduced for BT detection as well as classification based on traditional machine learning (ML) and deep learning (DL). The traditional ML classifiers require hand-crafted features, which is very time-consuming. On the contrary, DL is very robust in feature extraction and has recently been widely used for classification and detection purposes. Therefore, in this work, we propose a hybrid deep learning model called DeepTumorNet for three types of brain tumors (BTs)—glioma, meningioma, and pituitary tumor classification—by adopting a basic convolutional neural network (CNN) architecture. The GoogLeNet architecture of the CNN model was used as a base. While developing the hybrid DeepTumorNet approach, the last 5 layers of GoogLeNet were removed, and 15 new layers were added instead of these 5 layers. Furthermore, we also utilized a leaky ReLU activation function in the feature map to increase the expressiveness of the model. The proposed model was tested on a publicly available research dataset for evaluation purposes, and it obtained 99.67% accuracy, 99.6% precision, 100% recall, and a 99.66% F1-score. The proposed methodology obtained the highest accuracy compared with the state-of-the-art classification results obtained with Alex net, Resnet50, darknet53, Shufflenet, GoogLeNet, SqueezeNet, ResNet101, Exception Net, and MobileNetv2. The proposed model showed its superiority over the existing models for BT classification from the MRI images.