Backpropagation Neural Network for Book Classification Using the Image Cover

Artificial Neural Networks are known to provide a good model forclassification. The goal of this research is to classify books in Bahasa (Bahasa Indonesia) using its cover. The data is in the form of scanned images, each with the size of 300 cm height, 130 cm width, and 96 dpi image resolution the research conducted features extraction using image processing method, MSER (Maximally Stable Externally Regions) to identify the area of book title, and Tesseract Optical Character Recognition (OCR) to detect the title. Next, features extracted from MSER and OCR are converted into a numerical matrix as the input to the Backpropagation Artificial Neural Network. The accuracy obtained using one hidden layer and 15 neurons is 63.31%. Meanwhile, the evaluation using 2 hidden layers with a combination of 15 and 35 neurons resulted in accuracy of 79.89%. The ability of the model to classify the book was affected by the image quality, variation, and number of training data

Boosting microscopic object detection via feature activation map guided poisson blending

Microscopic examination of visible components based on micrographs is the gold standard for testing in biomedical research and clinical diagnosis. The application of object detection technology in bioimages not only improves the efficiency of the analyst but also provides decision support to ensure the objectivity and consistency of diagnosis. However, the lack of large annotated datasets is a significant impediment in rapidly deploying object detection models for microscopic formed elements detection. Standard augmentation methods used in object detection are not appropriate because they are prone to destroy the original micro-morphological information to produce counterintuitive micrographs, which is not conducive to build the trust of analysts in the intelligent system. Here, we propose a feature activation map-guided boosting mechanism dedicated to microscopic object detection to improve data efficiency. Our results show that the boosting mechanism provides solid gains in the object detection model deployed for microscopic formed elements detection. After image augmentation, the mean Average Precision (mAP) of baseline and strong baseline of the Chinese herbal medicine micrograph dataset are increased by 16.3% and 5.8% respectively. Similarly, on the urine sediment dataset, the boosting mechanism resulted in an improvement of 8.0% and 2.6% in mAP of the baseline and strong baseline maps respectively. Moreover, the method shows strong generalizability and can be easily integrated into any main-stream object detection model. The performance enhancement is interpretable, making it more suitable for microscopic biomedical applications

Transforming urinary stone disease management by artificial intelligence-based methods: A comprehensive review

Objective: To provide a comprehensive review on the existing research and evi-dence regarding artificial intelligence (AI) applications in the assessment and management of urinary stone disease.Methods: A comprehensive literature review was performed using PubMed, Scopus, and Google Scholar databases to identify publications about innovative concepts or supporting applica-tions of AI in the improvement of every medical procedure relating to stone disease. The terms "endourology", "artificial intelligence", "machine learning", and "urolithiasis"were used for searching eligible reports, while review articles, articles referring to automated procedures without AI application, and editorial comments were excluded from the final set of publica-tions. The search was conducted from January 2000 to September 2023 and included manu-scripts in the English language.Results: A total of 69 studies were identified. The main subjects were related to the detection of urinary stones, the prediction of the outcome of conservative or operative management, the optimization of operative procedures, and the elucidation of the relation of urinary stone chemistry with various factors.Conclusion: AI represents a useful tool that provides urologists with numerous amenities, which explains the fact that it has gained ground in the pursuit of stone disease management perfection. The effectiveness of diagnosis and therapy can be increased by using it as an alter-native or adjunct to the already existing data. However, little is known concerning the poten-tial of this vast field. Electronic patient records, containing big data, offer AI the opportunity to develop and analyze more precise and efficient diagnostic and treatment algorithms. Never-theless, the existing applications are not generalizable in real-life practice, and high-quality studies are needed to establish the integration of AI in the management of urinary stone dis-ease.CNN ; CNN

Missed urinary tract infection in patients with chronic recalcitrant LUTS and recurrent cystitis

Background: MSU culture and Urinary dipsticks as a diagnostic method for urinary infection (UTI) are discredited despite commonly used to exclude UTI in patients with lower urinary tract symptoms (LUTS). The phenotype of painful LUTS has been recast as Interstitial Cystitis (IC) or Bladder Pains Syndrome (BPS) because infection has been excluded on the evidence of these methods. Given that these all-important tests have been found insensitive and misleading, there is justification in re-examining IC/BPS to ascertain whether we have been mistaken. I studied patients with “Chronic recalcitrant bladder pain and recurrent cystitis” (abbreviated “painful LUTS”) who had been diagnosed with IC/PBS in order to re-assess their pathophysiology.// Aim: I characterised these patients using the scientific method of consilience, which scrutinised them from unrelated perspectives. These studies implied that infection was a most probable aetiological factor. Therefore, I moved on to test infection as a causal factor using Pearl’s three rungs of causation: Correlation, intervention and the counterfactual.// Methods: Data on quality of life and disease experience were obtained. Symptoms and pathophysiological variables in 146 patients presenting with painful LUTS were studied. To achieve Pearl’s specifications, an observational study studied intervention and a cross-over study analysed the counter factual of arbitrary treatment cessation. The evolution of treatment of these patients, using first generation, narrow spectrum urinary agents in protracted courses is reported. Since protracted antibiotic exposure is feared as a cause of antimicrobial resistance (AMR), I measured this in order to round off my findings// Results: The consilience studies incriminated UTI in the aetiology of painful LUTS. It is also clear that the patients suffer terribly, and this is aggravated by professional scepticism catalysed by a misinterpretation of urinalysis data. Antibiotic intervention demonstrated a regression in all disease indicators but there was resurgence of symptoms and signs during trials without treatment. The data on AMR demonstrated a rise in resistance in response to a first prescription without this increasing with persistence of the antibiotic regimen.// Conclusion: These data imply that IC/BPS (painful LUTS) is caused by a treatable urinary tract infection and are sufficient to merit a RCT. Whilst, treatment requires protracted exposure to antibiotics, my data on AMR amongst these patients is surprisingly reassuring. This requires further exploration. Contemporaneous to this thesis, other have published definitive data that refute urine culture and dipstick analysis./

2006 Annual Research Symposium Abstract Book

2006 annual volume of abstracts for science research projects conducted by students at Trinity College