Tuberculosis (TB) is is an infectious disease that generally attacks the lungs and causes death for millions of people annually. Chest radiography and deep-learning-based image segmentation techniques can be utilized for TB diagnostics. Convolutional Neural Networks (CNNs) has shown advantages in medical image recognition applications as powerful models to extract informative features from images. Here, we present a novel hybrid method for efficient classification of chest X-ray images. First, the features are extracted from chest X-ray images using MobileNet, a CNN model, which was previously trained on the ImageNet dataset. Then, to determine which of these features are the most relevant, we apply the Artificial Ecosystem-based Optimization (AEO) algorithm as a feature selector. The proposed method is applied to two public benchmark datasets (Shenzhen and Dataset 2) and allows them to achieve high performance and reduced computational time. It selected successfully only the best 25 and 19 (for Shenzhen and Dataset 2, respectively) features out of about 50,000 features extracted with MobileNet, while improving the classification accuracy (90.2% for Shenzen dataset and 94.1% for Dataset 2). The proposed approach outperforms other deep learning methods, while the results are the best compared to other recently published works on both datasets.

Accurate brain tumor segmentation from 3D Magnetic Resonance Imaging (3D-MRI) is an important method for obtaining information required for diagnosis and disease therapy planning. Variation in the brain tumor’s size, structure, and form is one of the main challenges in tumor segmentation, and selecting the initial contour plays a significant role in reducing the segmentation error and the number of iterations in the level set method. To overcome this issue, this paper suggests a two-step dragonfly algorithm (DA) clustering technique to extract initial contour points accurately. The brain is extracted from the head in the preprocessing step, then tumor edges are extracted using the two-step DA, and these extracted edges are used as an initial contour for the MRI sequence. Lastly, the tumor region is extracted from all volume slices using a level set segmentation method. The results of applying the proposed technique on 3D-MRI images from the multimodal brain tumor segmentation challenge (BRATS) 2017 dataset show that the proposed method for brain tumor segmentation is comparable to the state-of-the-art methods.

Aluminum alloys have low weldability by conventional fusion welding processes. Friction stir welding (FSW) is a promising alternative to traditional fusion welding techniques for producing high quality aluminum joints. The quality of the welded joints is highly dependent on the process parameters used during welding. In this research, a new approach was developed to predict the process parameters and mechanical properties of AA6061-T6 aluminium alloy joints in terms of ultimate tensile strength (UTS). A new hybrid artificial neural network (ANN) approach has been proposed in which Henry Gas Solubility Optimization (HGSO) algorithm has been incorporated to improve the performance of Random Vector Functional Link (RVFL) network. The HGSO-RVFL model was constructed with four parameters; rotational speed, welding speed, tilt angle, and pin profile. The validity of the model was tested, and it was demonstrated that the HGSO-RVFL model is a powerful technique for predicting the UTS of friction stir welded (FSWD) joints. In addition, the effects of process parameters on UTS of welded joints were discussed, where a significant agreement was observed between experimental results and predicted results which indicates the high performance of the model developed to predict the appropriate welding parameters that achieve optimal UTS.

The objective of this research is to build a "Whole Slide Images" classification system using Convolutional Neural Network (CNN). This system is capable of classifying Thyroid tumors into three types: Follicular adenoma, follicular carcinoma, and papillary carcinoma. Furthermore, the cascaded CNN technique is additionally employed to classify the classified follicular carcinoma into four subclasses: follicular carcinoma, papillary follicular variant, well-differentiated follicular carcinoma, and Poorlydifferentiated follicular carcinoma. Results of the proposed CNN architecture showed effective classification of Thyroid carcinoma in the whole slide images with an overall accuracy of 94.69%. In the first classification stage, the images are classified into either one of three main types with an overall accuracy of 98.74%, while in the second classification stage, using the cascaded CNN, accuracy was 95.90% for further subclassification into four sub-classes. Our cascaded CNN outperformed the accuracy of other studies due to splitting classification process of the thyroid into two stages which reduces the number of classes in each stage.INDEX TERMS Whole slide images, Convolutional neural network, Thyroid carcinoma, Data augmentation, Histopathology whole slide imaging.

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