In this study, we present an application of extreme learning machine (ELM) and image processing techniques for identifying butterfly species as an alternative to conventional diagnostic methods. This paper evaluates the capability of butterfly species classification by using texture features of butterfly images. Two texture descriptors such as grey-level co-occurrence matrix (GLCM) and local binary patterns (LBP) were used for comparison purpose. ELM is employed for classification in butterfly-feature space. A total of 190 butterfly images belonging to 19 different species of Pieridae family were used. The identification accuracy of the proposed method was 98.25% and 96.45% with GLCM and LBP butterfly-feature spaces, respectively. The methodology presented herein effectively detected and classified these butterflies. These findings suggested that the proposed GLCM, LBP texture features extraction techniques and ELM algorithm are feasible and excellent in identification and classification of butterfly species.

Declared a pandemic disease, COVID-19 has affected the lives of millions of people and had significant effects on public health. Despite the development of effective vaccines against COVID-19, cases continue to increase worldwide. According to studies in the literature, artificial intelligence methods are used effectively for the detection of COVID-19. In particular, deep-learning-based approaches have achieved very good results in clinical diagnostic studies and other fields. In this study, a new approach using x-ray images is proposed to detect COVID-19. In the proposed method, the angle transform (AT) method is first applied to the x-ray images. The AT method proposed in this study is an important novelty in the literature, as there is no such approach in previous studies. This transformation uses the angle information created by each pixel on the image with the surrounding pixels. Using the AT approach, eight different images are obtained for each image in the dataset. These images are trained with a hybrid deep learning model, which combines GoogleNet and long short-term memory (LSTM) models, and COVID-19 disease detection is carried out. A dataset from the Mendeley database is used to test the proposed approach. A high classification accuracy of 98.97% is achieved with the AT + GoogleNet + LSTM approach. The results obtained were also compared with other studies in the literature. The presented results reveal that the proposed method is successful for COVID-19 detection using chest x-ray images. Direct transfer methods were also applied to the data set used in the study. However, worse results were observed according to the proposed approach. The proposed approach has the flexibility to be applied effectively to different medical images.