A Recognizing Technique Specific Disease on a Chest X-Ray with Support for Image Clarity and Deep Learning
Keywords:
medical imaging, chest x-ray classifier, computer vision, CLAHE, SuCKAbstract
This study was to predict 14 demonstrative signs in 3 conspicuous public chest X-ray datasets: MIMIC-CXR, Chest-Xray8, CheXpert, as well as a multi-site conglomeration of each of these data sets.The multi-source data set is compared with the smallest inconsistency, recommending one method to reduce the slope. In this research experiment using 5 CNN models, where for pre-processing using the CLAHE and SuCK methods, which help make the image look clearer and more contrasting. Based on experiments on the chest thorax there are 3 datasets, namely 1000, 5000 and 10000 datasets, as well as 2 pre-processing methods, namely CLAHE and SuCK. After the experiment, the CNN model which has the highest accuracy was used using 1000 datasets with CLAHE namely the DenseNet 121 model at 95%, and SUCK at 100%, for the total 5000 data sets the highest accuracy was using CLAHE, namely the Resnet 50V2 model at 83% and with SuCK, namely DenseNet 121 by 86%. For the number of 10000 datasets, the highest accuracy with CLAHE is the Inception V3 model of 63%, while the SUCK model of ResNet 50V2 is 87%. With several experiments, it is proven that the SuCK method produces better accuracy than CLAHE. This research can be continued with the use of test images for a more diverse chest thorax.
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