A deep architecture based on attention mechanisms for effective end-to-end detection of early and mature malaria parasites

Malaria is a severe infectious disease caused by the Plasmodium parasite. The early and accurate detection of this disease is crucial to reducing the number of deaths it causes. However, the current method of detecting malaria parasites involves manual examination of blood smears, which is a time-consuming and labor-intensive process, mainly performed by skilled hematologists, especially in underdeveloped countries. To address this problem, we have developed two deep learning-based systems, YOLO-SPAM and YOLO-SPAM++, which can detect the parasites responsible for malaria at an early stage. Our evaluation of these systems using two public datasets of malaria parasite images, MP-IDB and IML, shows that they outperform the current state-of-the-art, with more than 11M fewer parameters than the baseline YOLOv5m6. YOLO-SPAM++ demonstrated a substantial 10% improvement over YOLO-SPAM and up to 20% against the best-performing baseline in preliminary experiments conducted on the Plasmodium Falciparum species of MP-IDB. On the other hand, YOLO-SPAM showed slightly better results than YOLO-SPAM++ in subsets without tiny parasites, while YOLO-SPAM++ performed better in subsets with tiny parasites, with precision values up to 94%. Further cross-species generalization validations, conducted by merging training sets of various species within MP-IDB, showed that YOLO-SPAM++ consistently outperformed YOLOv5 and YOLO-SPAM across all species, emphasizing its superior performance and precision in detecting tiny parasites. These architectures can be integrated into computer-aided diagnosis systems to create more reliable and robust systems for the early detection of malaria

A Shallow Learning Investigation for COVID-19 Classification

COVID-19, an infectious coronavirus disease, triggered a pandemic that resulted in countless deaths. Since its inception, clinical institutions have used computed tomography as a supplemental screening method to reverse transcription-polymerase chain reaction. Deep learning approaches have shown promising results in addressing the problem; however, less computationally expensive techniques, such as those based on handcrafted descriptors and shallow classifiers, may be equally capable of detecting COVID-19 based on medical images of patients. This work proposes an initial investigation of several handcrafted descriptors well known in the computer vision literature already been exploited for similar tasks. The goal is to discriminate tomographic images belonging to three classes, COVID-19, pneumonia, and normal conditions, and present in a large public dataset. The results show that kNN and ensembles trained with texture descriptors achieve outstanding accuracy in this task, reaching accuracy and F-measure of 93.05% and 89.63%, respectively. Although it did not exceed state of the art, it achieved satisfactory performance with only 36 features, enabling the potential to achieve remarkable improvements from a computational complexity perspective

SAMMI: Segment Anything Model for Malaria Identification

Malaria, a life-threatening disease caused by the Plasmodium parasite, is a pressing global health challenge. Timely detection is critical for effective treatment. This paper introduces a novel computer-aided diagnosis system for detecting Plasmodium parasites in blood smear images, aiming to enhance automation and accessibility in comprehensive screening scenarios. Our approach integrates the Segment Anything Model for precise unsupervised parasite detection. It then employs a deep learning framework, combining Convolutional Neural Networks and Vision Transformer to accurately classify malaria-infected cells. We rigorously evaluate our system using the IML public dataset and compare its performance against various off-the-shelf object detectors. The results underscore the efficacy of our method, demonstrating superior accuracy in detecting and classifying malaria-infected cells. This innovative Computer-aided diagnosis system presents a reliable and near real-time solution for malaria diagnosis, offering significant potential for widespread implementation in healthcare settings. By automating the diagnosis process and ensuring high accuracy, our system can contribute to timely interventions, thereby advancing the fight against malaria globally

Understanding cheese ripeness: An artificial intelligence-based approach for hierarchical classification

Within the contemporary dairy industry, the effective monitoring of cheese ripeness constitutes a critical yet challenging task. This paper proposes the first public dataset encompassing images of cheese wheels that depict various products at distinct stages of ripening and introduces an innovative hybrid approach, integrating machine learning and computer vision techniques to automate the detection of cheese ripeness. By leveraging deep learning and shallow learning techniques, the proposed method endeavors to overcome the limitations associated with conventional assessment methodologies. It aims to provide automation, precision, and consistency in the evaluation of cheese ripeness, delving into a hierarchical classification for the simultaneous classification of distinct cheese types and ripeness levels and presenting a comprehensive solution to enhance the efficiency of the cheese production process. By employing a lightweight hierarchical feature aggregation methodology, this investigation navigates the intricate landscape of preprocessing steps, feature selection, and diverse classifiers. We report a noteworthy achievement, attaining a best F-measure score of 0.991 through the merging of features extracted from EfficientNet and DarkNet-53, opening the field to concretely address the complexity inherent in cheese quality assessment

Turbulent flame shape switching at conditions relevant for gas turbines

Abstract A numerical investigation is conducted in this work to shed light on the reasons leading to different flame configurations in gas turbine combustion chambers of aeronautical interest. Large eddy simulations (LES) with a flamelet-based combustion closure are employed for this purpose to simulate the DLR-AT Big Optical Single Sector (BOSS) rig fitted with a Rolls-Royce developmental lean burn injector. The reacting flow field downstream this injector is sensitive to the intricate turbulent-combustion interaction and exhibits two different configurations: (i) a penetrating central jet leading to an M-shape lifted flame; or (ii) a diverging jet leading to a V-shaped flame. First, the LES results are validated using available BOSS rig measurements, and comparisons show that the numerical approach used is consistent and works well. The turbulent-combustion interaction model terms and parameters are then varied systematically to assess the flame behavior. The influences observed are discussed in the paper from physical and modelling perspectives to develop physical understanding on the flame behavior in practical combustors for both scientific and design purposes.Clean Sky 2 Joint Undertaking under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 686332

FLOW INHOMOGENEITIES IN A REALISTIC AERONAUTICAL GAS-TURBINE COMBUSTOR: FORMATION, EVOLUTION AND INDIRECT NOISE

Indirect noise generated by the acceleration of combustion inhomogeneities is an important aspect in the design of aeroengines because of its impact on the overall noise emitted by an aircraft and the possible contribution to combustion instabilities. In this study, a realistic rich-quench-lean combustor is numerically investigated, with the objective of quantitatively analyzing the formation and evolution of flow inhomogeneities and determine the level of indirect combustion noise in the nozzle guide vane (NGV). Both entropy and compositional noise are calculated in this work. A high-fidelity numerical simulation of the combustion chamber, based on the Large-Eddy Simulation (LES) approach with the Conditional Moment Closure (CMC) combustion model, is performed. The contribution of the different air streams to the formation of flow inhomogeneity is identified and separated through seven dedicated passive scalars. This pins down the individual contributions of the air streams to combustion inhomogeneity at the combustor’s exit. LES-CMC results are then used to determine the acoustic sources to feed an NGV aeroacoustic model, which outputs the noise generated by entropy and compositional inhomogeneities. Results show that non-negligible fluctuations of temperature and composition reach the combustor’s exit. Combustion inhomogeneities originate both from finite-rate chemistry effects and incomplete mixing. In particular, the role of mixing with dilution and liner air flows on the level of com-bustion inhomogeneities at the combustor’s exit is highlighted. The species that most contribute to indirect noise are identified and the transfer functions of a realistic NGV are computed. The noise level indicates that indirect noise generated by temperature fluctuations is larger that the indirect noise generated by compositional inhomogeneities, although the latter is not negligible and is expected to become louder in supersonic nozzles. It is also shown that relatively small fluctuations of the local flame structure can lead to significant variations of the nozzle transfer function, whose gain increases with the Mach number. This highlights the necessity of an on-line solution of the local flame structure, which is performed in this paper by CMC, for an accurate prediction of the level of compositional noise. This study opens new possibilities for the identification, separation and calculation of the sources of indirect combustion noise in realistic aeronautical gas turbines

An Anomaly Detection Approach to Determine Optimal Cutting Time in Cheese Formation

The production of cheese, a beloved culinary delight worldwide, faces challenges in maintaining consistent product quality and operational efficiency. One crucial stage in this process is determining the precise cutting time during curd formation, which significantly impacts the quality of the cheese. Misjudging this timing can lead to the production of inferior products, harming a company’s reputation and revenue. Conventional methods often fall short of accurately assessing variations in coagulation conditions due to the inherent potential for human error. To address this issue, we propose an anomaly-detection-based approach. In this approach, we treat the class representing curd formation as the anomaly to be identified. Our proposed solution involves utilizing a one-class, fully convolutional data description network, which we compared against several stateof-the-art methods to detect deviations from the standard coagulation patterns. Encouragingly, our results show F1 scores of up to 0.92, indicating the effectiveness of our approach

Dynamic surface electromyography using stretchable screen-printed textile electrodes

Objective. Wearable devices have created new opportunities in healthcare and sport sciences by unobtrusively monitoring physiological signals. Textile polymer-based electrodes proved to be effective in detecting electrophysiological potentials but suffer mechanical fragility and low stretch resistance. The goal of this research is to develop and validate in dynamic conditions cost-effective and easily manufacturable electrodes characterized by adequate robustness and signal quality. Methods. We here propose an optimized screen printing technique for the fabrication of PEDOT:PSS-based textile electrodes directly into finished stretchable garments for surface electromyography (sEMG) applications. A sensorised stretchable leg sleeve was developed, targeting five muscles of interest in rehabilitation and sport science. An experimental validation was performed to assess the accuracy of signal detection during dynamic exercises, including sit-to-stand, leg extension, calf raise, walking, and cycling. Results. The electrodes can resist up to 500 stretch cycles. Tests on five subjects revealed excellent contact impedance, and cross-correlation between sEMG envelopes simultaneously detected from the leg muscles by the textile and Ag/AgCl electrodes was generally greater than 0.9, which proves that it is possible to obtain good quality signals with performance comparable with disposable electrodes. Conclusions. An effective technique to embed polymer-based electrodes in stretchable smart garments was presented, revealing good performance for dynamic sEMG detections. Significance. The achieved results pave the way to the integration of unobtrusive electrodes, obtained by screen printing of conductive polymers, into technical fabrics for rehabilitation and sport monitoring, and in general where the detection of sEMG in dynamic conditions is necessary

Percutaneous Vertebral Reconstruction (PVR) Technique of Pathological Compression Fractures: An Innovative Combined Treatment of Microwave Ablation, Bilateral Expandable Titanium SpineJack Implants Followed by Vertebroplasty

Background: to retrospectively evaluate safety and efficacy of combined microwave ablation (MWA) and bilateral expandable titanium SpineJack (SJ) implants followed by vertebroplasty (VP) for the treatment of painful thoracolumbar pathological vertebral compression fracture. (2) Methods: from July 2017 to October 2022, twenty-eight patients (13 women and 15 men; mean age 68 ± 11 years) with a history of primary neoplasm and thirty-six painful vertebral metastases with vertebral compression fracture underwent combined MWA and bilateral expandable titanium SpineJack implants with vertebroplasty. We analyzed safety through complications rate, and efficacy through vertebral height restoration and pain decrease, evaluated using a visual analogue scale (VAS), and Functional Mobility Scale (FMS), and local tumor control. Contrast-enhanced CT scans were performed at 1, 3, and 6 months and a contrast-enhanced spine MRI at 6 months after the procedure. (3) Results: Technical success rate was 100%. No procedure-related major complications or death occurred. Vertebral height restoration was observed in 22 levels (58%), with a mean anterior height restoration of 2.6 mm ± 0.6 and a mean middle height restoration of 4.4 mm ± 0.6 (p < 0.001). Mean VAS score of pain evaluation on the day before treatment was 6.3 ± 1.5 (range 4–9). At the 6-month evaluation, the median VAS score for pain was 0.4 ± 0.6 (range 0–2) with a mean reduction of 93.65% (6.8 ± 0.7 vs. 0.4 ± 0.6; p < 0.000) compared with baseline evaluation. Contrast-enhanced CT scans were performed at 1, 3, and 6 months and a contrast-enhanced spine MRI was performed at 6 months after the procedure, showing no local recurrence, implant displacement, or new fractures in the treated site. (4) Conclusions: combined microwave ablation and bilateral expandable titanium SpineJack implants with vertebroplasty is a safe and effective procedure for the treatment of pathological compressive vertebral fractures. The vertebral stabilization achieved early and persistent pain relief, increasing patient mobility, improving recovery of walking capacity, and providing local tumor control

Non-invasive Coronary Flow Velocity Reserve Assessment Predicts Adverse Outcome In Women With unstable angina Without Obstructive Coronary Artery Stenosis

Background: Evaluation of coronary flow velocity reserve (CFVR) is the physiological approach to assess the severity of coronary stenosis and microvascular dysfunction. Impaired CFVR occurs frequently in women with suspected or known coronary artery disease . The aim of this study was to assess the role of CFVR to predict long-term cardiovascular event rate in women with unstable angina (UA) without obstructive coronary artery stenosis. Methods: CFVR in left anterior descending coronary artery was assessed by adenosine transthoracic echocardiograhy in 161 women admitted at our Department with UA and without obstructive coronary artery disease. Results: During a mean FU of 32.5 ±19.6 months, 53 cardiac events occurred: 6 nonfatal acute myocardial infarction , 22 UA, 7 coronary revascularization by percutaneous transluminal coronary angioplasty, 1 coronary bypass surgery, 3 ischemic stroke and 8 episodes of congestive heart failure with preserved ejection fraction and 6 cardiac deaths. Using a ROC curve analysis, CFVR 2.14 was the best predictor of cardiac events and was considered as abnormal CFVR. Abnormal CFVR was associated with lower cardiac event-free survival (30% vs 80%, p<0.0001). During FU, 70% of women with reduced CFVR had cardiac events whereas only 20% with normal CFVR (p=0.0001). At multivariate Cox analysis, smoke habitus (p=0.003), metabolic syndrome (p=0.01), and CFVR (p<0.0001) were significantly associated with cardiac events at FU. Conclusion: Noninvasive CFVR provides an independent predictor of cardiovascular prognosis information in women with UA without obstructive coronary artery disease whereas, impaired CFVR seems to be associated with higher CV events at FU