Breast cancer is the most common cancer among women and is the second leading cause of death. There is currently no efficient way to prevent breast cancer, but its detection in early stages can increase the patient's chances of being cured and surviving. Computer-aided diagnosis (CAD) systems, based on image processing techniques, can provide a more reliable interpretation of mammographic images to detect microcalcifications and have been able to identify and classify benign and malignant tumors. If we are dealing with a massive number of images, this system increases the ability and accuracy of detection. Also, in cases where the number of images is not large, CAD systems can significantly improve the image quality. In addition, a CAD system can identify suspicious areas to provide radiologists with a visual aid to interpret mammograms. Deep Learning and convolutional neural networks have recently shown significant performance for visual applications. Convolutional neural networks have also been used efficiently to analyze medical images and diagnose mammograms. In this paper, a CAD system based on convolutional neural networks (Mask R-CNN) with Multi-task Learning to detect breast cancer and segment mammogram images is proposed. The Mask-RCNN technique is one of the strongest and most flexible deep grids ever designed for machine vision. In this article, multitask Learning with the integration of two tasks of classification and segmentation is used to diagnose breast cancer. R-CNN convolution neural network is used to diagnose cancerous mass. This system consists of two main stages, including the production of pseudo-color image and segmentation-detection based on convolutional neural networks (R-CNN Mask). The INbreast dataset is employed for evaluation of the proposed method.

Glaucoma stands out as a prevalent ocular ailment in the elderly population, causing substantial harm to the optic nerves and eventual vision impairment. Fundus photography plays a pivotal role in the clinical assessment of glaucoma, facilitating the exploration of associated morphological alterations. Computational algorithms, capable of processing fundus images, have emerged as indispensable tools in this diagnostic domain. Hence, the imperative development of an automated diagnostic system leveraging image processing techniques is underscored. In this study, a novel approach to the segmentation and classification of retinal optic nerve head images is introduced. This method concurrently executes both tasks through a deep Learning framework, thereby enhancing the Learning speed within the network. The proposed network encompasses approximately 29 million parameters and demonstrates an efficiency of 2.5 seconds for segmenting and classifying retinal images. Central to this strategy is a Multi-task deep Learning network, harmonizing segmentation and classification processes, and leveraging information from both tasks to optimize Learning efficacy. Validation of the proposed method is conducted using the publicly available ORIGA dataset. The attained performance metrics for accuracy, sensitivity, specificity, and F1-score are 99.461, 93.46, 100, and 98.7006, respectively. These results collectively affirm the substantial advancement achieved by the proposed method in comparison to existing methodologies.

Distributed Denial of Service (DDoS) attacks are among the primary concerns in internet security today. Machine Learning can be exploited to detect such attacks. In this paper, a multi-layer perceptron model is proposed and implemented using deep machine Learning to distinguish between malicious and normal traffic based on their behavioral patterns. The proposed model is trained and tested using the CICDDoS2019 dataset. To remove irrelevant and redundant data from the dataset and increase Learning accuracy, feature selection is used to select and extract the most effective features that allow us to detect these attacks. Moreover, we use the grid search algorithm to acquire optimum values of the model’s hyperparameters among the parameters’ space. In addition, the sensitivity of accuracy of the model to variations of an input parameter is analyzed. Finally, the effectiveness of the presented model is validated in comparison with some state-of-the-art works.

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