Abstract Brain tumor detection can make the difference between life and death. Recently, deep learning-based brain tumor detection techniques have gained attention due to their higher performance. However, obtaining the expected performance of such deep learning-based systems requires large amounts of classified images to train the deep models. Obtaining such data is usually boring, time-consuming, and can easily be exposed to human mistakes which hinder the utilization of such deep learning approaches. This paper introduces a novel framework for brain tumor detection and classification. The basic idea is to generate a large synthetic MRI images dataset that reflects the typical pattern of the brain MRI images from a small class-unbalanced collected dataset. The resulted dataset is then used for training a deep model for detection and classification. Specifically, we employ two types of deep models. The first model is a generative model to capture the distribution of the important features in a set of small class-unbalanced brain MRI images. Then by using this distribution, the generative model can synthesize any number of brain MRI images for each class. Hence, the system can automatically convert a small unbalanced dataset to a larger balanced one. The second model is the classifier that is trained using the large balanced dataset to detect brain tumors in MRI images. The proposed framework acquires an overall detection accuracy of 96.88% which highlights the promise of the proposed framework as an accurate low-overhead brain tumor detection system.

Abstract A new generalized framework for lung cancer detection and classification are introduced in this paper. Specifically, two types of deep models are presented. The first model is a generative model to capture the distribution of the important features in a set of small class-unbalanced collected CXR images. This generative model can be utilized to synthesize any number of CXR images for each class. For example, our generative model can generate images with tumors with different sizes and positions in the lung. Hence, the system can automatically convert the small unbalanced collected dataset to a larger balanced one. The second model is the ResNet50 that is trained using the large balanced dataset for cancer classification into benign and malignant. The proposed framework acquires 98.91% overall detection accuracy, 98.85% area under curve (AUC), 98.46% sensitivity, 97.72% precision, 97.89% F1 score. The classifier takes 1.2334 s on average to classify a single image using a machine with 13GB RAM.

Abstract Skin cancer becomes a significant health problem worldwide with an increasing incidence over the past decades. Due to the fine-grained differences in the appearance of skin lesions, it is very challenging to develop an automated system for benign-malignant classification through images. This paper proposes a novel automated Computer Aided Diagnosis (CAD) system for skin lesion classification with high classification performance using accuracy low computational complexity. A pre-processing step based on morphological filtering is employed for hair removal and artifacts removal. Skin lesions are segmented automatically using Grab-cut with minimal human interaction in HSV color space. Image processing techniques are investigated for an automatic implementation of the ABCD (asymmetry, border irregularity, color and dermoscopic patterns) rule to separate malignant melanoma from benign lesions. To classify skin lesions into benign or malignant, different pretrained convolutional neural networks (CNNs), including VGG-16, ResNet50, ResNetX, InceptionV3, and MobileNet are examined. The average 5-fold cross validation results show that ResNet50 architecture combined with Support Vector Machine (SVM) achieve the best performance. The results also show the effectiveness of data augmentation in both training and testing with achieving better performance than obtaining new images. The proposed diagnosis framework is applied to real clinical skin lesions, and the experimental results reveal the superior performance of the proposed framework over other recent techniques in terms of area under the ROC curve 99.52%, accuracy 99.87%, sensitivity 98.87%, precision 98.77%, F1 -score 97.83%, and consumed time 3.2 s. This reveals that the proposed framework can be utilized to help medical practitioners in classifying different skin lesions.

Two new encryption algorithms for secure video transmission are proposed in this paper. The two algorithms employ different types of chaotic maps to generate the keystream for encrypting the video frames. Both algorithms involve a substitution step and a permutation step to achieve confusion and diffusion requirements. For efficient transmission, the video file is compressed before being encrypted. In the basic implementation of both algorithms, MPEG‐2 standard is used for compression. However, the algorithms are shown to be compliant with other compression techniques. In the permutation step, the effect of the block size used in the shuffling process is examined. Smaller blocks result in increasing the processing time, while reducing both the correlation between adjacent pixels and the peak‐signal‐to noise ratio of an encrypted frame. The use of a Feistel structure is investigated to enhance security and its negative impact on the encryption time is demonstrated. The experimental results of the two proposed schemes confirm that they represent different tradeoffs between security and computational efficiency. Both schemes are sensitive to slight variations in the encryption key as apparent from the obtained differential measures. The conducted comparative study shows the competitiveness of the proposed schemes to existing schemes in literature.

AbstractBreast cancer is a major cause of transience amongst women. In this paper, two novel techniques, ResNet50 and VGG‐16, are utilised and re‐trained to recognise two classes rather than 1000 classes with high accuracy and low computational requirements. In addition, transfer learning and data augmentation are performed to solve the problem of lack of tagged data. To get a better accuracy, the support vector machine (SVM) classifier is utilised instead of the last fully connected layer. Our models performance are verified utilising k ‐fold cross‐validation. Our proposed techniques are trained and evaluated on three mammographic datasets: mammographic image analysis society, digital database for screening mammography (DDSM) and the curated breast imaging subset of DDSM. This paper explains end‐to‐end fully convolutional neural networks without any prepossessing or post‐processing. The proposed technique of employing ResNet50 hybridised with SVM achieves the best performance, specifically with the DDSM dataset, producing 97.98% accuracy, 98.46% area under the curve, 97.63% sensitivity, 96.51% precision, 95.97% F1 score and computational time 1.8934 s.