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BackgroundTuberculosis (TB) is one of the most infectious diseases that can be fatal. Its early diagnosis and treatment can significantly reduce the mortality rate. In the literature, several computer-aided diagnosis (CAD) tools have been proposed for the efficient diagnosis of TB from chest radiograph (CXR) images. However, the majority of previous studies adopted conventional handcrafted feature-based algorithms. In addition, some recent CAD tools utilized the strength of deep learning methods to further enhance diagnostic performance. Nevertheless, all these existing methods can only classify a given CXR image into binary class (either TB positive or TB negative) without providing further descriptive information. ObjectiveThe main objective of this study is to propose a comprehensive CAD framework for the effective diagnosis of TB by providing visual as well as descriptive information from the previous patients’ database. MethodsTo accomplish our objective, first we propose a fusion-based deep classification network for the CAD decision that exhibits promising performance over the various state-of-the-art methods. Furthermore, a multilevel similarity measure algorithm is devised based on multiscale information fusion to retrieve the best-matched cases from the previous database. ResultsThe performance of the framework was evaluated based on 2 well-known CXR data sets made available by the US National Library of Medicine and the National Institutes of Health. Our classification model exhibited the best diagnostic performance (0.929, 0.937, 0.921, 0.928, and 0.965 for F1 score, average precision, average recall, accuracy, and area under the curve, respectively) and outperforms the performance of various state-of-the-art methods. ConclusionsThis paper presents a comprehensive CAD framework to diagnose TB from CXR images by retrieving the relevant cases and their clinical observations from the previous patients’ database. These retrieval results assist the radiologist in making an effective diagnostic decision related to the current medical condition of a patient. Moreover, the retrieval results can facilitate the radiologists in subjectively validating the CAD decision.

BackgroundThe early diagnosis of various gastrointestinal diseases can lead to effective treatment and reduce the risk of many life-threatening conditions. Unfortunately, various small gastrointestinal lesions are undetectable during early-stage examination by medical experts. In previous studies, various deep learning–based computer-aided diagnosis tools have been used to make a significant contribution to the effective diagnosis and treatment of gastrointestinal diseases. However, most of these methods were designed to detect a limited number of gastrointestinal diseases, such as polyps, tumors, or cancers, in a specific part of the human gastrointestinal tract. ObjectiveThis study aimed to develop a comprehensive computer-aided diagnosis tool to assist medical experts in diagnosing various types of gastrointestinal diseases. MethodsOur proposed framework comprises a deep learning–based classification network followed by a retrieval method. In the first step, the classification network predicts the disease type for the current medical condition. Then, the retrieval part of the framework shows the relevant cases (endoscopic images) from the previous database. These past cases help the medical expert validate the current computer prediction subjectively, which ultimately results in better diagnosis and treatment. ResultsAll the experiments were performed using 2 endoscopic data sets with a total of 52,471 frames and 37 different classes. The optimal performances obtained by our proposed method in accuracy, F1 score, mean average precision, and mean average recall were 96.19%, 96.99%, 98.18%, and 95.86%, respectively. The overall performance of our proposed diagnostic framework substantially outperformed state-of-the-art methods. ConclusionsThis study provides a comprehensive computer-aided diagnosis framework for identifying various types of gastrointestinal diseases. The results show the superiority of our proposed method over various other recent methods and illustrate its potential for clinical diagnosis and treatment. Our proposed network can be applicable to other classification domains in medical imaging, such as computed tomography scans, magnetic resonance imaging, and ultrasound sequences.

Global climate variations, mainly extreme temperatures reduce crop production and contributed to global food insecurity. Plant growth regulators (PGRs) improve physiological efficiency, photosynthetic capacity, and assimilate partitioning in field crops. However, their influence on soybean (Glycine max L.) yield and seed quality is under-researched. This study investigates the synergistic effects of auxin (2,4-D) and gibberellin (GA3) at 60 and 90 mg L−1 concentrations. This study investigates the impact of phytohormones, specifically gibberellic acid (GA3) and 2,4-dichlorophenoxyacetic acid (2,4-D), on the morphological and biochemical traits of soybean (Glycine max L.) through seed treatment and foliar application. These Growth regulators were applied through seed treatment at the time of sowing and foliar application at the time of flowering initiation stage. The experiment was designed using a RCBD arrangement and each block replicated thrice time. Seed treatment with 2,4-D at 90 mg/L showed that maximum plant height (116.80 cm), first node height (8.04 cm), No. of branches per plant (18.67), root length (18.12 cm) stem diameter (0.05 cm), no. of pods per plant (32.60), seed yield (1532.30 kg ha−1), biological yield (4109.30 ha−1), and harvest index (39.36 %). The GA3 (seed treatment) at conc. of 90 and 60 mg L−1 resulted in a significantly increased no. of nodule fresh weight, nodules per plant, and dry weight respectively. But the no. of seeds per pod did not showed significant results throughout the study. However, the foliar applied of 2,4-D at 90 mg L−1 resulted in significantly increased leaf area index (LAI) compared to the seed treatment. PGRs had a significant influence on quality traits. Thus, the application notably improved the potential of soybean and successful oilseed crop. Therefore, it is showed that using PGRs are extremely helpful for attaining higher growth, yield production, and improved quality of soybean.

Climate change is one of the main challenges, and it poses a tough challenge to the agriculture industry globally. Additionally, greenhouse gas (GHG) emissions are the main contributor to climate change; however, croplands are a prominent source of GHG emissions. Yet this complex challenge can be mitigated through climate-smart agricultural practices. Conservation tillage is commonly known to preserve soil and mitigate environmental change by reducing GHG emissions. Nonetheless, there is still a paucity of information on the influences of conservation tillage on wheat yield, soil properties, and GHG flux, particularly in the semi-arid Dingxi belt. Hence, in order to fill this gap, different tillage systems, namely conventional tillage (CT) control, straw incorporation with conventional tillage (CTS), no-tillage (NT), and stubble return with no-tillage (NTS), were laid at Dingxi, Gansu province of China, under a randomized complete block design with three replications to examine their impacts on yield, soil properties, and GHG fluxes. Results depicted that different conservative tillage systems (CTS, NTS, and NT) significantly (p