International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 10 Issue: 05 | May 2023
p-ISSN: 2395-0072
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APPLICATION OF CNN MODEL ON MEDICAL IMAGE Shubham Verma1, Kushagra Anand2, Kartik Verma3, Ms. Deepti Gupta (Guide) 4, Ms. Kavita Saxena(Co-Guide)5 1Shubham Verma, Dept. of Computer Science Engineering, Maharaja Agrasen Institute of Technology, Delhi, India
2Kushagra Anand, Dept. of Computer Science Engineering, Maharaja Agrasen Institute of Technology, Delhi, India 3Kartik Verma, Dept. of Computer Science Engineering, Maharaja Agrasen Institute of Technology, Delhi, India 4Assistant Professor Ms.Deepti Gupta, Dept. of Computer Science Engineering, Maharaja Agrasen Institute of
Technology, Delhi, India
5Assistant Professor Ms.Kavita Saxena, Dept. of Computer Science Engineering, Maharaja Agrasen Institute of
Technology, Delhi, India ---------------------------------------------------------------------***--------------------------------------------------------------------Abstract - There have been several studies that have used Deep learning technique offer a potential solution to
improve the accuracy and efficiency of disease diagnosis. These techniques involve traininga model on a large dataset of labelled images, where the model learns to recognize patterns and features that are indicative of diseases related to chest. One approach that has been widely used is transfer learning, which involves pre- training a model on a large dataset and then fine-tuning it on a smaller, specific dataset for a particular task.
deep learning techniques to detect disease from medical images such as chest X-rays or CT scans. These techniques involve training a model on a large dataset of labeled images, where the model learns to recognize patterns and features that are indicative of disease related to chest infection. One example of a study that used deep learning for this purpose was published in the journal Radiology in 2017. In this study, the authors trained a convolutional neural network (CNN) on a dataset of chest X-rays and found that the CNN was able to accurately classify images as normal or infected with an AUC (area under the curve) of 0.97. Another example is a study published in the journal Chest in 2018. The authors found that their model had an accuracy of 89.6% and an AUC of 0.94. Overall, the use of deep learning and for disease detection shows promising results and has the potential to improve the accuracy and efficiency of the diagnosis process.
CNN Architectures has been applied to disease detection using chest X-rays with promising results. For example, a study published in the journal Radiology in 2017 used a convolutional neural network (CNN) trained on a large dataset of chest X-rays and found that the CNN was able to accurately classify images as normal or Disease with an AUC (area under the curve) of 0.97. Overall, the use of deep learning for disease detection using chest X-rays as the dataset shows promise as a way to improve the accuracy and efficiency of diagnosis and has the potential to benefit patients and healthcare systems.
Key Words: Deep Learning, CNN, Chest X-Ray Images. 1. INTRODUCTION
2. DATASET
Traditionally, Various diseases have been diagnosed using clinical symptoms, physical examination, and imaging tests such as chest X-rays. However, these methods can be subjective and may not always provide accurate results.
The Lung Infection in Chest X-ray Images dataset: This dataset contains over 3453 chest X-ray images. It has been widely used in research studies. Overall, these datasets provide a diverse range of chest X-ray images that can be used to train and evaluate models for disease detection. Chest X-ray images (anterior-posterior) were selected from retrospective cohorts of pediatric patients one to five years old from Guangzhou Women and Children’s Medical Center, Guangzhou. Analysis of chest x-ray images was done on all chest radiographs that were initially screened for quality control by removing all low-quality or unreadable x-ray images. The diagnoses for the images were then graded by two expert physicians before being cleared for training in the AI system. To check the grading errors, the evaluation set was confirmed by a third expert.
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