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The purpose of this work is to classify pepper seeds using color filter array (CFA) images. This study focused specifically on Penja pepper, which is found in the Litoral region of Cameroon and is a type of Piper nigrum. India and Brazil are the largest producers of this variety of pepper, although the production of Penja pepper is not as significant in terms of quantity compared to other major producers. However, it is still highly sought after and one of the most expensive types of pepper on the market. It can be difficult for humans to distinguish between different types of peppers based solely on the appearance of their seeds. To address this challenge, we collected 5618 samples of white and black Penja pepper and other varieties for classification using image processing and a supervised machine learning method. We extracted 18 attributes from the images and trained them in four different models. The most successful model was the support vector machine (SVM), which achieved an accuracy of 0.87, a precision of 0.874, a recall of 0.873, and an F1-score of 0.874. [ABSTRACT FROM AUTHOR]

Introduction: Image quality and acquisition protocol adherence assessment is a neglected area in teledermatology. We examine if it is feasible to use deep learning methods to automate the assessment of the adherence of examinations to image acquisition protocols. In this study, we focused on the quality criteria of two image acquisition protocols: (1) approximation image and (2) panoramic image, as these are present in all teledermatology examination protocols currently used by the Santa Catarina State Integrated Telemedicine and Telehealth System (STT/SC). Methods: We use a data set of 36,102 teledermatological examinations performed at the STT/SC during 2021. As our validation process, we adopted standard machine learning metrics and an inter-rater agreement (IRA) study with 11 dermatologists. For the approximation image protocol, we used the Mask-Region based Convolutional Neural Network (RCNN) Object Detection Deep Learning (DL) architecture to identify the presence of a lesion identification tag and a ruler used to provide a frame reference of the lesion. For the panoramic image protocol, we used DensePose, a pose estimation DL, architecture to assess the presence of a whole patient body and its orientation. A combination of the two approaches was additionally validated through an IRA study between specialists. Results: Mask-RCNN achieved a score of 96% mean average precision (mAP), while DensePose presented 75% mAP. IRA achieved a level of agreement of 96.68% with the Krippendorff alpha score. Conclusions: Our results show the feasibility of using deep learning to automate the image quality and protocol adherence assessment in teledermatology, before the specialist's manual analysis of the examination.