Deep Learning Based Automated COVID-19 Classification from Computed Tomography Images

A method of a Convolutional Neural Networks (CNN) for image classification with image preprocessing and hyperparameters tuning was proposed. The method aims at increasing the predictive performance for COVID-19 diagnosis while more complex model architecture. Firstly, the CNN model includes four similar convolutional layers followed by a flattening and two dense layers. This work proposes a less complex solution based on simply classifying 2D-slices of Computed Tomography scans. Despite the simplicity in architecture, the proposed CNN model showed improved quantitative results exceeding state-of-the-art when predicting slice cases. The results were achieved on the annotated CT slices of the COV-19-CT-DB dataset. Secondly, the original dataset was processed via anatomy-relevant masking of slice, removing none-representative slices from the CT volume, and hyperparameters tuning. For slice processing, a fixed-sized rectangular area was used for cropping an anatomy-relevant region-of-interest in the images, and a threshold based on the number of white pixels in binarized slices was employed to remove none-representative slices from the 3D-CT scans. The CNN model with a learning rate schedule and an exponential decay and slice flipping techniques was deployed on the processed slices. The proposed method was used to make predictions on the 2D slices and for final diagnosis at patient level, majority voting was applied on the slices of each CT scan to take the diagnosis. The macro F1 score of the proposed method well-exceeded the baseline approach and other alternatives on the validation set as well as on a test partition of previously unseen images from COV-19CT-DB dataset

Becoming a young farmer in the digital age— An island perspective

This study investigates the career construction paths of young farmers and aims to contribute to the literature on the “young farmer problem.” Of particular relevance is this study’s focus on the potential of islands as a new career landscape in the digital age. Young farmers’ subjective experiences toward careers were analyzed based on narrative interviews, quantitative surveys and expert interviews from two EU islands: Crete and the Azores. Firstly, the study provides insights on the behavioral and cognitive dimensions of the career construction model by identifying followed career paths. Secondly, we turn our focus to the role of digital communications in career construction and, thirdly, the study examines the geographical dimension of the model. We find that involvement with farming entails complex career patterns that evolve into passion. Whether their involvement follows planned or unplanned paths, protean career attitudes, desire to experiment, and a strong sense of career self-concept play significant roles in shaping the career narratives. “Experience” and “management” dimensions of online communication drive the construction of careers as a part of a professional identity mechanism. Our results reveal that the “island effect” (maintaining a part-time farming culture) plays a role in cohesive singular and multiple career self-concepts.info:eu-repo/semantics/publishedVersio

Economic crisis and labour force transition to inactivity: a comparative study in German rural and urban areas

This study analyses the determinants of labour force transition to inactivity in the German labour market. Using German Labour Force Survey data the influence on the transition flow to inactivity of factors such as age, education, marital status, sex and registration with the public employment service are examined. We present estimates of degree of urbanisation-specific multinominal logit models to analyse the determinants of individuals’ transition probabilities in rural and urban areas. By comparing the influence of the factors that affect transition to inactivity before (2002-07) and during (2008-09) the global economic crisis, this paper contributes to the general understanding of transitional labour market flow dynamics during the crisis period. The findings suggest that during the crisis period education level and marital status have had different impacts in rural and urban regions on the transition to inactivity. While these two factors influenced the transition to inactivity before the crisis, their effect has been stronger during it. Additionally the results suggest that the interaction of individuals with institutional settings (e.g. registration with the public employment service) have to be taken into account when designing active labour market policy measures, especially during crisis periods. Knowledge about the influence of these factors on the transition to inactivity, and their different effects in rural and urban areas, provides important information for designing policies aiming to reduce the transition to inactivity during crisis periods

Proximal tibial osteotomies for the medial compartment arthrosis of the knee: a historical journey

Several proximal tibial osteotomy techniques for the medial compartment arthrosis of the knee are described and traced in their development. These techniques are of the closed wedge, dome and open wedge types. We detail the differences in planning and surgery as well the need for different fixation devices. This historical and technical description will benefit those surgeons wishing to undertake the procedure as an alternative to joint replacement strategies

Automatic annotation of X-ray images: a study on attribute selection

Advances in the medical imaging technology has lead to an exponential growth in the number of digital images that need to be acquired, analyzed, classified, stored and retrieved in medical centers. As a result, medical image classification and retrieval has recently gained high interest in the scientific community. Despite several attempts, the proposed solutions are still far from being sufficiently accurate for real-life implementations. In a previous work, performance of different feature types were investigated in a SVM-based learning framework for classification. of X-Ray images into classes corresponding to body parts and local binary patterns were observed to outperform others. In this paper, we extend that work by exploring the effect of attribute selection on the classification performance. Our experiments show that principal component analysis based attribute selection manifests prediction values that are comparable to the baseline (all-features case) with considerably smaller subsets of original features, inducing lower processing times and reduced storage space

Covid-19 detection using modified xception transfer learning approach from computed tomography images

The significance of efficient and accurate diagnosis amidst the unique challenges posed by the COVID-19 pandemic underscores the urgency for innovative approaches. In response to these challenges, we propose a transfer learning-based approach using a recently annotated Computed Tomography (CT) image database. While many approaches propose an intensive data preprocessing and/or complex model architecture, our method focuses on offering an efficient solution with minimal manual engineering. Specifically, we investigate the suitability of a modified Xception model for COVID-19 detection. The method involves adapting a pre-trained Xception model, incorporating both the architecture and pre-trained weights from ImageNet. The output of the model was designed to make the final diagnosis decisions. The training utilized 128 batch sizes and 224x224 input image dimensions, downsized from standard 512x512. No further da processing was performed on the input data. Evaluation is conducted on the 'COV19-CT-DB' CT image dataset, containing labeled COVID-19 and non-COVID-19 cases. Results reveal the method's superiority in accuracy, precision, recall, and macro F1 score on the validation subset, outperforming the VGG-16 transfer model and thus offering enhanced precision with fewer parameters. Furthermore, compared to alternative methods for the COV19-CT-DB dataset, our approach exceeds the baseline approach and other alternatives on the same dataset. Finally, the adaptability of the modified Xception transfer learning-based model to the unique features of the COV19-CT-DB dataset showcases its potential as a robust tool for enhanced COVID-19 diagnosis from CT images

Medical image retrieval and automatic annotation: VPA-SABANCI at ImageCLEF 2009

Advances in the medical imaging technology has lead to an exponential growth in the number of digital images that needs to be acquired, analyzed, classified, stored and retrieved in medical centers. As a result, medical image classification and retrieval has recently gained high interest in the scientific community. Despite several attempts, such as the yearly-held ImageCLEF Medical Image Annotation Competition, the proposed solutions are still far from being su±ciently accurate for real-life implementations. In this paper we summarize the technical details of our experiments for the ImageCLEF 2009 medical image annotation task. We use a direct and two hierarchical classification schemes that employ support vector machines and local binary patterns, which are recently developed low-cost texture descriptors. The direct scheme employs a single SVM to automatically annotate X-ray images. The two proposed hierarchi-cal schemes divide the classification task into sub-problems. The first hierarchical scheme exploits ensemble SVMs trained on IRMA sub-codes. The second learns from subgroups of data defined by frequency of classes. Our experiments show that hier-archical annotation of images by training individual SVMs over each IRMA sub-code dominates its rivals in annotation accuracy with increased process time relative to the direct scheme

Permen Biji semangka

ABSTRAKBiji semangka tanpa sepengetahuan masayarakat ternyata memiliki nilai gizi yang tinggi yang dibutuhkan oleh tubuh yaitu karbohidrat, fenol flavonoid, protein, serat, fosfor, dan  zat besi. Dilihat dari manfaat biji semangka maka di buat PERMEN BISMA (Permen Biji Semangka). Permen biji semangka ini bertujuan untuk menciptakan alternatif pangan untuk meminimalisir limbah biji semangka yang terbuang sia-sia untuk dijadikan suatu produk yang bermanfaat, bernilai ekonomi, dan sebagai cemilan sehat yang kaya akan kandungan gizi serta berkembangnya produk ini diharapkan mampu membuka lapangan pekerjaan bagi masyarakat menengah ke bawah, dan dapat meningkatkan nilai ekonomi limbah biji semangka, serta membuat maha siswa semakin kreatif dalam bidang kewirausahaan.Metode pembuatan permen biji semangka tidak jauh berbeda dengan metode pembuatan permen  lainnya hanya saja bahannya yang berbeda. Permen ini menggunakan biji semangka yang sudah tidak layak di makan namun bijinya masih tetap segar.Produk olahan ini di pasarkan di warung, stand, media online dan sekolahan terdekat. Target pemasaran permen ini untuk semua usia. Produk PERMEN BISMA ini di pasarkan seharga Rp. 12.000/bungkus. Produk yang dihasilkan dalam satu periode masa produksi adalah 1000 bungkus. Dengan biaya produksi sebesar Rp.5.000.000 diperoleh keuntungan dalam satu periode sebesar Rp. 2.810.500. Pembuatan PERMEN BISMA ini menumbuhkan kreativitas mahasiswa dalam bidang berwirausaha dan membuka lapangan pekerjaan baru

RAGAM BASA SUNDA LISAN SISWA KELAS XI SMAN I CIBUNGBULANG KACAMATAN CIBUNGBULANG KABUPATEN BOGOR : ulikan adegan leksikal jeung morfologis

Ieu panalungtikan boga tujuan pikeun ngadéskripsikeun ragam basa Sunda lisan siswa kelas XI SMAN 1 Cibungbulang Kacamatan Cibungbulang Kabupaten Bogor, anu nyoko kana adegan leksikal, adegan morfologis, jeung faktor-faktor nu mangaruhan ayana campur kode. Ieu panalungtikan maké pamarekan kualitatif kalawan métode deskriptif nu ngagunakeun purposive sampling, nangtukeun 16 urang siswa ti kelas XI IPA jeung IPS nu maké basa Sunda lisan dina situasi nonformal. Data dikumpulkeun maké téhnik observasi, ngarékam paguneman siswa nu satuluyna ditranskripsikeun, sarta ngagunakeun angkét pikeun maluruh faktor-faktor nu mangaruhan ayana campur kode. Cara ngolah data ngagunakeun téhnik unsur langsung. Hasil panalungtikan kapanggih unsur léksikal nu mangrupa kecap saharti (sinonim) aya 14 kecap jeung kecap wancahan nu mangrupa kecap tingkesan, singgetan, jeung tangkesan. Ragam basa Sunda lisan téh umumna (5,39%) mangrupa kecap panambah, kecap pananya (2,27%), kecap panyeluk (3,13%). Adegan morfologis ragam lisan siswa umumna (15,66%) mangrupa kecap rundayan, kecap rajékan (2,27%), pangsaeutikna kecap kantétan (0,85%). Campur kode nu kapanggih umumna (55,81%) tina basa Indonésia, anu pangsaeutikna (2,32%) asalna tina basa Jawa jeung Arab, tina basa Betawi jeung Inggris masing-masing (16,27%), tina basa Slang (6,97%). Faktor-faktor nu mangaruhan ayana campur kode (88%) pangaweruh siswa nu hésé néangan kecap-kecap nu merenah dina basa Sunda (88%) dina nyarita sapopoé ngagunakéun basa campuran (Indonesia jeung Sunda), sarta (56,8%) asal séké sélér bapana lain ti Sunda. ;---Penelitian ini bertujuan untuk mendeskripsikan ragam bahasa Sunda lisan yang digunakan oleh siswa kelas XI SMAN I Cibungbulang Kecamatan Cibungbulang Kabupaten Bogor. Penelitian ini lebih memfokuskan pada penggunaan kata bahasa Sunda ragam lisan dalam struktur leksikal dan struktur morfologis, serta faktor-faktor yang mempengaruhi campur kode. Metode yang digunakan dalam penelitian ini adalah metode deskriptif dengan pendekatan kualitatif. Untuk teknik pengumpulan data menggunakan purposive sampling dengan menentukan 16 orang siswa dari kelas XI IPA dan IPS. Sumber data dalam penelitian ini adalah para siswa yang menggunakan bahasa Sunda lisan dalam situasi nonformal. Data dikumpulkan menggunakan teknik observasi, merekam percakapan siswa dan angket. Unsur leksikal yang ditemukan merupakan 14 kata sinonim dan penyingkatan kata berjumlah 17. Ragam bahasa Sunda lisan pada umumnya (5,39%) merupakan kecap panambah, kecap pananya (2,27%), dan kecap panyeluk (3,12%). Berdasarkan bentuk morfologis ragam lisan pada umumnya (15,66%) merupakan kecap rundayan, (2,27%) kecap rajékan dan (0,85%) kecap kantétan. Campur kode yang dilakukan pada umumnya berasal dari kosakata bahasa Indonesia sebanyak (55,81%), dari bahasa Betawi dan bahasa Inggris masing-masing (16,27%), dari bahasa Slang (6,97%), serta dari bahasa Jawa dan bahasa Arab masing-masing (2,32%). Faktor penyebab terjadinya campur kode adalah (88%) siswa menyatakan sulit mendapatkan padanan kata yang tepat dalam bahasa Sunda, (88%) siswa dalam melakukan komunikasi menggunakan bahasa campuran (bahasa Indonesia dan bahasa Sunda), serta (56,8%) asal suku orang tua siswa bukan dari suku Sunda.;---The study aims to describe The Sundanese word verbal variety used by the eleventh grade’s students of SMAN I Cibungbulang, Cibungbulang District Bogor Regency. This study focuses more on the uses of The Sundanese word verbal variety in lexical and morphological structures, as well as factors affecting code mixing.The method used in this research is descriptive method with qualitative approach. For data collecting techniques, the writer records the students' conversations and transcribe them while the questionnaire technique is used to determine the factors causing the interference code. Sampling technique in this research used purposive sampling with 16 students from eleventh’s grade from natural and social programs. From the data analysis was found that 302 sentences consisting of 352 words used by students during the conversation. Of the total words were found that The Sundanese word verbal variety consist of interjection (kecap panyeluk) (3,31%), adders (kecap panambah) (5,39%), and the question words (2,27%). Based on the formation of word (morphological process) was found the Sundanese word verbal variety, which in general form of affixed words (rundayan) (15,66%), repetitious words (rajékan) (2,27%), conjunctions (kantétan) (0,85%), and the abbreviations (4,84%). The vocabulary of the most mixed code comes from the Indonesian vocabulary (55,81%). From the analysis of the questionnaires indicated that the factors causing the mixed code were (88%) said it was difficult to get the exact equivalent of the word in Sundanese language, (88%) in communication using mixed language (Indonesian and Sundanese languages),(56,8%) from tribe of students father is not from Sundanese tribe

Profunda femoris artery pseudoaneurysm after surgery and trauma

Pseudoaneurysms of the profunda femoris artery have been reported following different types of trauma and from orthopedic procedures performed in the proximal femur. Two cases of profunda femoris artery pseudoaneurysm with two rare causes are presented. The first one is a core decompression of femoral head for osteonecrosis and the second one is a proximal femur fracture nailing. Awareness and careful follow-up are the key issues for the early diagnosis