Chest Disease Detection Using Deep Learning System: A Review

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@article{1712446,
      author = {R. Prasanth Reddy, Nagavelli Yogender Nath, Gattu Ramya, Syed Abdul Haq},
      title = {Chest Disease Detection Using Deep Learning System: A Review},
      journal = {Iconic Research And Engineering Journals},
      year = {2024},
      volume = {7},
      number = {7},
      pages = {737-745},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1712446.pdf},
      abstract = {Healthcare industry serves as a major sector of economy of every country. It is a well-defined practice that started several years ago. As the time passed the improvement in healthcare sector has touched the new edges and of course technology has always played a very important role in it. Each and every phase of the healthcare development has different issue with them. When it comes to the prehistoric era, when medications were not yet created, healing with herbs and other natural resources was a lengthy process that may take years to complete. Now-a-days with advancement of machines and technology although the time involved in treatment has decreased but the problem arises to store data and records of several patients, records, treatments and many more. This review paper work focuses on the detection of several chest diseases including lymphoma disease. As we realize that chest diseases are so regular now-a-days, it's basic to successfully predict and analyze them. The study's dataset of several chest x-ray pictures was analyzed. Images from people with a total of fourteen different types of chest disorders?including atelectasis, consolidation, infiltration, and pneumothorax?as well as a class dubbed "No findings" if the condition was undetected?were included in the study. Consequently, the classification report states that the VGG-19 model is the most effective deep and federated transfer-learning model.},
      keywords = {Chest Diseases, Deep Learning, X-Ray, Lymphoma},
      month = {January},
      doi = {https://doi.org/10.64388/IREV7I7-1712446}
  }