this research represents a method to detect malaria parasite in blood samples stained with giemsa. In order to increase the accuracy of detecting, at the first step, the red blood cell mask is extracted. It is due to the fact that most of malaria parasites exist in red blood cells. Then, stained elements of blood such as red blood cells, parasites and white blood cells are extracted. At the next step, red blood cell mask is located on the extracted stained elements to separate the possible parasites. Finally, color histogram, granulometry, gradient and flat texture features are extracted and used as classifier inputs. Here, five classifiers were used: support vector machines (SVM), nearest mean (NM), K nearest neighbors (KNN), 1-NN, and Fisher. In this research K nearest neighbors classifier had the best accuracy, which was 91%.

Chronic wounds are ulcerations of the skin that fail to heal because of an underlying condition such as diabetes mellitus or venous insufficiency. The timely identification of this condition is crucial for healing. However, this identification requires expert knowledge unavailable in some care situations. Here, artificial intelligence technology may support clinicians. In this study, we explore the performance of a deep convolutional neural network to classify diabetic foot and venous leg ulcers using wound images. We trained a convolutional neural network on 863 cropped wound images. Using a hold-out test set with 80 images, the model yielded an F1-score of 0.85 on the cropped and 0.70 on the full images. This study shows promising results. However, the model must be extended in terms of wound images and wound types for application in clinical practice.

Venous leg ulcers and diabetic foot ulcers are the most common chronic wounds. Their prevalence has been increasing significantly over the last years, consuming scarce care resources. This study aimed to explore the performance of detection and classification algorithms for these types of wounds in images. To this end, algorithms of the YoloV5 family of pre-trained models were applied to 885 images containing at least one of the two wound types. The YoloV5m6 model provided the highest precision (0.942) and a high recall value (0.837). Its mAP_0.5:0.95 was 0.642. While the latter value is comparable to the ones reported in the literature, precision and recall were considerably higher. In conclusion, our results on good wound detection and classification may reveal a path towards (semi-) automated entry of wound information in patient records. To strengthen the trust of clinicians, we are currently incorporating a dashboard where clinicians can check the validity of the predictions against their expertise.

This paper proposed a new method for testing digital circuits without hardware implementation. This data-based method detects hundreds of single stuck-at faults in the ALU circuits, utilizing deep stacked-sparse-autoencoder (SSAE). ATALANTA software is one of the free automatic test pattern generation tools which cover faults in high accuracy. Test vectors which are extracted from bench circuits via ATALANTA software are the key point of the paper. Fault detection is introduced as a two-class problem. SSAE network is trained using the test vectors. Dimension reduction is done automatically in SSAE. Network performance is tested by changing sparse coefficients, number of stacked autoencoder and data augmentation. The results of this step are compared with the traditional multilayer perceptron classification. In this method, unlike SSAE, a manual method of reducing the dimension and extracting the feature is used. Fault coverage of ATALANTA software is over than 94%. Finally, the results obtained from the deep neural network show its significant performance in the circuit faults detection automatically.

Background: The main method used for the laboratory confirmation of malaria is the conventional light microscopy; however, microscopy has three main disadvantages: I) it is time-consuming and labor-intensive; II) its results depend heavily on good techniques, reagents and microscopes; III) in many cases decisions about treatment are often taken without using the result of microscopy because of long delays in providing the results to the clinician. Hence, an extreme necessity of the fast automatic detection of the disease is required to diagnose and treat promptly. Objectives: Through the improvement of classification accuracy rate, this work aims to present a computer-assisted diagnosis system for malaria parasite. Materials and Methods: This study was conducted using 400 confirmed images of blood slides infected with malaria parasite. The MATLAB software was used for the implementation of computation procedures. Using five extracted features (flat texture, saturation channel histogram, color histogram, gradient, and granulometry) and six classifiers (k-Nearest Neighbors (k-NN), 1-Nearest Neighbor (1-NN), decision tree (DT), Fisher, linear discriminant analysis (LDA), and quadratic discriminant analysis (QDA)), images were classified into two classes: parasitic and nonparasitic. Then, classifier fusion was done using several algorithms: mean, min, max, stack, median, Adaboost, and bagging. Results: Using six classifiers separately, the highest accuracy was obtained 92% using the k-NN classifier. The highest accuracy of the classifiers' fusion was obtained using the Adaboost algorithm with 95.5% success rate. Conclusions: By comparing the results of classification using multiple classifier fusion with respect to using each classifier separately, it is found that the classifier fusion is more effective in enhancing the detection accuracy.