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INTERNATIONAL JOURNAL OF SCIENTIFIC DEVELOPMENT AND RESEARCH International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2455-2631 | Impact factor: 8.15 | ESTD Year: 2016 open access , Peer-reviewed, and Refereed Journals, Impact factor 8.15 |
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| Paper Title: | Analysis of Medical Image Through Convolutional Neural Network In Machine Learning |
| Authors Name: | Manikanta Macha , Varun Tej Madduluri , Vivek Lingampelly , Ajay Kumar Malepu , Benitha Sowmiya E |
| Unique Id: | IJSDR2404069 |
| Published In: | Volume 9 Issue 4, April-2024 |
| Abstract: | Pneumonia is a serious respiratory infection that has high rates of morbidity and mortality worldwide. It is particularly dangerous for people that are already at risk. In order to improve patient outcomes through diagnosis and treatment, it is imperative that pneumonia be identified from medical pictures as soon as possible and accurately. Promising outcomes have been observed in medical image analysis with recent deep learning breakthroughs such as CNNs and You Only Look Once (YOLO) item detection. In order to distinguish between pneumonia and healthy instances, our approach uses CNN-based classification after initially using YOLO to identify possible pneumonia regions in chest X-ray images. Experiments conducted on benchmark datasets show that our technique is effective in correctly identifying pneumonia from chest X-ray pictures. Our method provides a reliable and effective solution for automated pneumonia screening by combining CNN classification with YOLO object recognition, enabling prompt diagnosis and intervention in clinical settings. In order to detect pneumonia in chest X-rays, a convolutional neural network model for image analysis is proposed in this overview. The concept is intended to address the reliability and interpretation issues that are frequently present in medical diagnosis. Our methodology uses advanced deep learning algorithms to automatically highlight regions of interest in chest X-ray images and detect the presence of pneumonia, in contrast to previous methods that mostly rely on manual inspection. By improving diagnostic performance and accuracy, this methodology seeks to help medical practitioners make better decisions. By utilising ensemble approaches and data augmentation techniques, our model is able to identify pneumonia and evaluate related illnesses like pleurisy with remarkable robustness and accuracy. |
| Keywords: | Machine Learning, You Only Look Once, Convolutional Neural Network, Accuracy, Prediction, Pneumonia, Detection, Deep Learning, |
| Cite Article: | "Analysis of Medical Image Through Convolutional Neural Network In Machine Learning", International Journal of Science & Engineering Development Research (www.ijsdr.org), ISSN:2455-2631, Vol.9, Issue 4, page no.452 - 458, April-2024, Available :http://www.ijsdr.org/papers/IJSDR2404069.pdf |
| Downloads: | 000338172 |
| Publication Details: | Published Paper ID: IJSDR2404069 Registration ID:210781 Published In: Volume 9 Issue 4, April-2024 DOI (Digital Object Identifier): Page No: 452 - 458 Publisher: IJSDR | www.ijsdr.org ISSN Number: 2455-2631 |
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