A rare case of appendicular skeleton localization in a patient with chronic lymphocytic leukemia successfully treated with salvage radiation therapy

Bone involvements in chronic lymphocytic leukemia (CLL) are considered rare events, and the English-language medical literature describes them only in sporadic case reports. Consequently, robust indications for a rational clinical management are lacking. We report the case of a middle-aged man in clinical follow-up for CLL who experienced pain at the right tibial level that was refractory to nonsteroidal anti-inflammatory drugs and an acute episode of anemia. Instrumental examinations and a bioptic sample surprisingly demonstrated a bone tibial localization by CLL

A fuzzy-enhanced deep learning approach for early detection of Covid-19 pneumonia from portable chest X-ray images

The Covid-19 pandemic is the defining global health crisis of our time. Chest X-Rays (CXR) have been an important imaging modality for assisting in the diagnosis and management of hospitalised Covid-19 patients. However, their interpretation is time intensive for radiologists. Accurate computer aided systems can facilitate early diagnosis of Covid-19 and effective triaging. In this paper, we propose a fuzzy logic based deep learning (DL) approach to differentiate between CXR images of patients with Covid-19 pneumonia and with interstitial pneumonias not related to Covid-19. The developed model here, referred to as CovNNet, is used to extract some relevant features from CXR images, combined with fuzzy images generated by a fuzzy edge detection algorithm. Experimental results show that using a combination of CXR and fuzzy features, within a deep learning approach by developing a deep network inputed to a Multilayer Perceptron (MLP), results in a higher classification performance (accuracy rate up to 81%), compared to benchmark deep learning approaches. The approach has been validated through additional datasets which are continously generated due to the spread of the virus and would help triage patients in acute settings. A permutation analysis is carried out, and a simple occlusion methodology for explaining decisions is also proposed. The proposed pipeline can be easily embedded into present clinical decision support systems