Pneumonia Detection Using Chest Radiographs with Novel EfficientNetV2L Model
Keywords:
Pneumonia detection, transfer learning, efficientnetv2l, NasNetMobile, chest X-rays, Yolo, Disease ClassificationAbstract
Pneumonia is a possibly lethal irresistible disease normally diagnosed utilizing actual assessments and diagnostic imaging techniques, such "chest X-rays", ultrasounds, or lung biopsies. Precise conclusion is fundamental, as mistaken diagnoses, lacking treatment, or nonattendance of treatment can bring about extreme repercussions for patients, including deadly results. This review presents an original strategy for "pneumonia location" and order, using "deep learning" models to improve diagnostic precision. The proposed technique utilizes refined models to classify pneumonia and recognize lung inconsistencies from clinical imaging, offering a more effective and robotized elective for medical services specialists. "NasNetMobile" accomplishes great accuracy in characterization undertakings, accomplishing "99.5%" across all actions. In object identification, YOLOv5s6 has extraordinary execution, accomplishing 100 percent accuracy and recall, as well as a 99.5% “mean average precision (mAP)", showing its greatness in restricting and recognizing "pneumonia-related peculiarities" in clinical pictures. The discoveries show that these models considerably further develop pneumonia location accuracy, introducing an important asset for early determination and proficient treatment. This strategy means to improve the symptomatic interaction and lift patient results by limiting human blunder and diagnostic deferrals.
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Dataset Link: https://www.kaggle.com/datasets/paultimothymooney/chest-xray-pneumonia
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