Titlebook: Machine Intelligence and Emerging Technologies; First International Md. Shahriare Satu,Mohammad Ali Moni,Mohammad Sham Conference proceedi

有毒

A Reliable and?Efficient Transfer Learning Approach for?Identifying COVID-19 Pneumonia from?Chest X- pathogenic laboratory testing, but it has a high risk of false negatives, forcing the development of additional diagnostic approaches to combat the disease. X-ray imaging is a straightforward and patient-friendly operation that may be performed in almost any healthcare facility. The aim of the repo

nurture

忍耐

Convolutional Neural Network Model to?Detect COVID-19 Patients Utilizing Chest X-Ray Images X-ray datasets of COVID-19 infected patients from Kaggle and Github, and pre-processed it using random sampling. Then, we proposed an enhanced convolutional neural network (CNN) model to this dataset and obtained a 94.03% accuracy, 95.52% AUC and 94.03% f-measure for detecting COVID-19 patients. We

fledged

Classification of?Tumor Cell Using a?Naive Convolutional Neural Network Modelight, radon gas, infectious agents etc. To diagnose the tumor cell promptly nowadays computer-aided detection (CAD) systems using a convolutional neural network (CNN) draws a significant role in the health sector. Many complicated CNN model has been introduced to effectively classify tumor cell but

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Tumor-TL: A Transfer Learning Approach for Classifying Brain Tumors from MRI Imageshanisms that normally govern normal cells. The identification and segmentation of brain tumors are among the most common difficult and time-consuming tasks when processing medical images. MRI is a medical imaging technique that allows radiologists to see within body structures without requiring surg

耕種

Deep Convolutional Comparison Architecture for Breast Cancer Binary Classificationtly leads to pathologists disagreeing. Recently, much research has tried to develop the best breast cancer classification models to help pathologists make more precise diagnoses. Consequently, convolutional networks are prominent in biomedical imaging because they discover significant features and a

HEW

intertwine

Brain Tumor Detection Using Deep Network EfficientNet-B0ge. However, catching a brain tumor with a bare eye could sometimes lead to misguidance or be costly to find someone who is a master in this field. So, the deep learning (DL) method is a boon for detecting tumors from images in the health sector. Here, we are going to propose a DL-based modified mod

LUT

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