For mobile clients, sufficient resources with the assurance of efficient performance and energy efficiency are the core concerns. This article mainly considers this need and proposes a resourceful architecture, called mRARSA that addresses the critical need in a mobile cloud environment. This architecture consists of cloud resources, mobile devices, and a set of functional components. The performance efficiency evaluates implementing the proposed context-aware multi-criteria decision offloading algorithm. This algorithm considers both device context (network parameters) and application content (task size) at run time when offloading an executable code to allocate the cloud resources. The appropriate resources select based on offloading decisions and via the wireless communication channels. The architecture's remarkable component is the signal strength analyzer that determines the signal quality (e.g.-60 dBm) and contributes to performance efficiency. The proposed prototype model has implemented several times to monitor the performance efficiency, mobility, performance at communication barriers, and the outcomes of resource-demanding application's execution. Results indicate performance improvement, such as the algorithm appropriately decides the cloud resources based on device network context, application content, mobility, and the signal strength quality and range. Moreover, the results also show significant improvement in achieving performance and energy efficiency. Sufficient resources and performance efficiency are the most significant features that distinguish this framework from the other existing frameworks.

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Leukemia is a fatal category of cancer-related disease that affects individuals of all ages, including children and adults, and is a significant cause of death worldwide. Particularly, it is associated with White Blood Cells (WBC), which is accompanied by a rise in the number of immature lymphocytes and cause damage to the bone marrow and/or blood. Therefore, a rapid and reliable cancer diagnosis is a critical requirement for successful therapy to raise survival rates. Currently, a manual analysis of blood samples obtained through microscopic images is done to diagnose this disease, which is often very slow, time-consuming, and less accurate. Furthermore, in microscopic analysis, the appearance and shape of leukemic cells seem very similar to normal cells which make detection more difficult. In the past decades, deep learning utilizing Convolutional Neural Networks (CNN) has provided state-of-the-art approaches for image classification problems; however, there is still a gap to improve their efficacy, learning procedure, and performance. Therefore, in this research study, we proposed a new variant of deep learning algorithm to diagnose leukemia disease by analyzing the microscopic images of blood samples. The proposed deep learning architecture emphasizes the channel associations on all levels of feature representation by incorporating the squeeze and excitation learning that recursively performs recalibration on channel-wise feature outputs by modeling channel interdependencies explicitly. In addition, the incorporation of the squeeze-and-excitation process enhances the feature discriminability of leukemic and normal cells, and strategically assists in exposing informative features of leukemia cells while suppressing less valuable ones as well as improving feature representational power of deep learning algorithm. We show that piling these learning operations of squeeze and excite together in a deep learning model can improve the performance of the model in diagnosing leukemia from microscopic images based on blood samples of patients. Furthermore, an extensive set of experiments are performed on both cropped cells and full-size microscopic images as well as with data augmentation to address the problem of fewer data and to further boost their performance. The proposed model is tested on two publicly available datasets of blood samples of leukemia patients, namely, ALL_IDB1 and ALL_IDB2. The suggested deep learning model exhibits good results and can be utilized to make a reliable computer-aided diagnosis for leukemia cancer.

A typical growth of cells inside tissue is normally known as a nodular entity. Lung nodule segmentation from computed tomography (CT) images becomes crucial for early lung cancer diagnosis. An issue that pertains to the segmentation of lung nodules is homogenous modular variants. The resemblance among nodules as well as among neighboring regions is very challenging to deal with. Here, we propose an end-to-end U-Net-based segmentation framework named DA-Net for efficient lung nodule segmentation. This method extracts rich features by integrating compactly and densely linked rich convolutional blocks merged with Atrous convolutions blocks to broaden the view of filters without dropping loss and coverage data. We first extract the lung’s ROI images from the whole CT scan slices using standard image processing operations and k-means clustering. This reduces the search space of the model to only lungs where the nodules are present instead of the whole CT scan slice. The evaluation of the suggested model was performed through utilizing the LIDC-IDRI dataset. According to the results, we found that DA-Net showed good performance, achieving an 81% Dice score value and 71.6% IOU score.

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