This paper proposes automated identification and classification of various stages of focal liver lesions based on the Multi-Support Vector Machine (Multi-SVM). The proposed system can be used to discriminate focal liver diseases such as Cyst, Hemangioma, and Hepatocellular carcinoma along with normal liver. The multi-class scenario is a composition of a series of two-class problems, using oneagainst-all which is the earliest and one of the most widely used implementations. We formulate the discrimination between cysts, cavernous hemangioma, hepatocellular carcinoma, and normal tissue as a supervised learning problem, and apply Multi-SVM to classify the diseases using Haralick local texture descriptors and histogram based features calculated from Regions Of Interest (ROIs), as input. Selection of ROI significantly impact the classification performances, thus we proposes an automatic ROI selection using Fuzzy c-means initialized by level set technique. For multi-class classification, we adopt the One-Against-All (OAA) method. The proposed Multi-SVM based CAD system using 10-fold cross validation yielded classification accuracy of 96.11% with the individual class accuracy of 97.78%, 95.56%, 93.33% and 97.78% for NOR, Cyst, HEM and HCC cases respectively. The proposed Multi-SVM based system is compared with the K-Nearest Neighbor (KNN) based approaches. Experimental results have demonstrated that the Multi-SVM based system greatly outperforms KNN-based approaches and other methods in the literature. The good performance of the proposed method shows a reliable indicator that can improve the information in the staging of focal liver lesion diseases. General TermsMedical Image Processing, Image Features and Analysis, and Liver diagnosis.

An improved classification technique is presented to identify automatically the acute lymphatic leukemia (ALL) subtypes. An adaptive segmentation procedure is performed on peripheral blood smear images to extract the main features (10 geometric features) from the segmented images of white blood cell (WBC), nucleus, and cytoplasm. To show the importance of the different extracted features for the diagnostic accuracy, a comprehensive study is made on all the possible permutation cases of the features using powerful classifiers which are K‐nearest neighbor (KNN) at different metric functions, support vector machine (SVM) with different kernels, and artificial neural network (ANN). This procedure enables us to construct a feature map depending only on least number of features which lead to the highest diagnostic accuracy. It is found that the features map regarding the vacuoles in the cytoplasm and the regularity of the nucleus membrane gives the highest accurate results. The automatic classification for ALL subtypes based only on these two effective features is assessed using the receiver operating characteristic (ROC) curve and F1‐score measures. It is confirmed that the present technique is highly accurate, and saves the effort and time of training.

No abstract

A modified digital image processing technique is presented to accurately investigate the types of the acute lymphatic leukaemia (ALL). In this technique, three complementary steps are performed. In the first one, a colour segmentation procedure is used to obtain images including only the white blood cell. In the second step, the histogram equalisation and linear contrast stretching procedures are utilised to obtain images for the nucleus. In the third step, images for the cytoplasm only may be reconstructed from which the vacuoles may be detected. For accurate detection for ALL types, significant and discriminative parameters are introduced such as geometric shape of nucleus membrane, equivalent sizes for the nucleus and cytoplasm and their ratio when the shapes of nucleuses are regular or irregular. This method is applied to a blood smear images for real cases of ALL. To validate the present technique, a comparison is made between present results with their counterparts obtained by expert (manual) technique. Another assessment is performed by comparing the average accuracy of the present technique and the average accuracy of different image processing techniques in the literature. The assessment confirms the high efficiency of the present technique in detecting all types of ALL.

Device-to-device (D2D) communication system is considered one of the most effective technology directions for solving the spectrum scarcity problem in 5G wireless communication. In this study, an electronic relay is proposed to be utilised in D2D communication. The electronic relay is a hybrid cooperative relay consisting of compress and forward relay with and without coding techniques, decode and forward relay and amplify and forward relay with variable gain. The selection of the most appropriate one has performed automatically according to the feedback channel state information. Additionally, the multipleinput multiple-output (MIMO) electronic relay with the multiuser and massive MIMO destination is introduced. The closed formulas of the outage probability for both cellular user and D2D transmission are derived. Furthermore, the analytical discussions for the proposed electronic relay are executed. Extensive MATLAB simulation programmes are executed to study the performance of the proposed system and to compare it with the conventional one. Moreover, the average sum rate is derived in terms of a number of electronic relay antennas and the D2D density through cellular networks.