Unveiling the Complexity of Medical Imaging through Deep Learning Approaches
Recent advancements in deep learning, particularly convolutional networks, have rapidly become the preferred methodology for analyzing medical images, facilitating tasks like disease segmentation, classification, and pattern quantification. Central to these advancements is the capacity to leverage h...
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| Format: | Article |
| Language: | English |
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Akif AKGUL
2023-12-01
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| Series: | Chaos Theory and Applications |
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| Online Access: | https://dergipark.org.tr/en/download/article-file/3261574 |
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| author | Javaid Iqbal Bhat Novsheena Rasool |
| author_facet | Javaid Iqbal Bhat Novsheena Rasool |
| author_sort | Javaid Iqbal Bhat |
| collection | DOAJ |
| description | Recent advancements in deep learning, particularly convolutional networks, have rapidly become the preferred methodology for analyzing medical images, facilitating tasks like disease segmentation, classification, and pattern quantification. Central to these advancements is the capacity to leverage hierarchical feature representations acquired solely from data. This comprehensive review meticulously examines a variety of deep learning techniques applied across diverse healthcare domains, delving into the intricate realm of medical imaging to unveil concealed patterns through strategic deep learning methodologies. Encompassing a range of diseases, including Alzheimer’s, breast cancer, brain tumors, glaucoma, heart murmurs, retinal microaneurysms, colorectal liver metastases, and more, the analysis emphasizes contributions succinctly summarized in a tabular form.The table provides an overview of various deep learning approaches applied to different diseases, incorporating methodologies, datasets, and outcomes for each condition.Notably, performance metrics such as accuracy, specificity, sensitivity, and other crucial measures underscore the achieved results. Specifically, an in-depth discussion is conducted on the Convolutional Neural Network (CNN) owing to its widespread adoption as a paramount tool in computer vision tasks. Moreover, an exhaustive exploration encompasses deep learning classification approaches, procedural aspects of medical image processing, as well as a thorough examination of key features and characteristics. At the end, we delve into a range of research challenges and put forth potential avenues for future improvements in the field. |
| format | Article |
| id | doaj-art-57d40165c864463dbc6f19c0fee372f5 |
| institution | Kabale University |
| issn | 2687-4539 |
| language | English |
| publishDate | 2023-12-01 |
| publisher | Akif AKGUL |
| record_format | Article |
| series | Chaos Theory and Applications |
| spelling | doaj-art-57d40165c864463dbc6f19c0fee372f52025-01-23T18:15:39ZengAkif AKGULChaos Theory and Applications2687-45392023-12-015426728010.51537/chaos.13267901971Unveiling the Complexity of Medical Imaging through Deep Learning ApproachesJavaid Iqbal Bhat0https://orcid.org/0000-0003-0312-4888Novsheena Rasool1https://orcid.org/0000-0001-6405-6415Islamic University of Science & Technology,Kashmir,India.Islamic University of Science and Technology, Kashmir, IndiaRecent advancements in deep learning, particularly convolutional networks, have rapidly become the preferred methodology for analyzing medical images, facilitating tasks like disease segmentation, classification, and pattern quantification. Central to these advancements is the capacity to leverage hierarchical feature representations acquired solely from data. This comprehensive review meticulously examines a variety of deep learning techniques applied across diverse healthcare domains, delving into the intricate realm of medical imaging to unveil concealed patterns through strategic deep learning methodologies. Encompassing a range of diseases, including Alzheimer’s, breast cancer, brain tumors, glaucoma, heart murmurs, retinal microaneurysms, colorectal liver metastases, and more, the analysis emphasizes contributions succinctly summarized in a tabular form.The table provides an overview of various deep learning approaches applied to different diseases, incorporating methodologies, datasets, and outcomes for each condition.Notably, performance metrics such as accuracy, specificity, sensitivity, and other crucial measures underscore the achieved results. Specifically, an in-depth discussion is conducted on the Convolutional Neural Network (CNN) owing to its widespread adoption as a paramount tool in computer vision tasks. Moreover, an exhaustive exploration encompasses deep learning classification approaches, procedural aspects of medical image processing, as well as a thorough examination of key features and characteristics. At the end, we delve into a range of research challenges and put forth potential avenues for future improvements in the field.https://dergipark.org.tr/en/download/article-file/3261574deep learningcomplexitycnnmedical imageanalysispattern recognitionsegmentation |
| spellingShingle | Javaid Iqbal Bhat Novsheena Rasool Unveiling the Complexity of Medical Imaging through Deep Learning Approaches Chaos Theory and Applications deep learning complexity cnn medical imageanalysis pattern recognition segmentation |
| title | Unveiling the Complexity of Medical Imaging through Deep Learning Approaches |
| title_full | Unveiling the Complexity of Medical Imaging through Deep Learning Approaches |
| title_fullStr | Unveiling the Complexity of Medical Imaging through Deep Learning Approaches |
| title_full_unstemmed | Unveiling the Complexity of Medical Imaging through Deep Learning Approaches |
| title_short | Unveiling the Complexity of Medical Imaging through Deep Learning Approaches |
| title_sort | unveiling the complexity of medical imaging through deep learning approaches |
| topic | deep learning complexity cnn medical imageanalysis pattern recognition segmentation |
| url | https://dergipark.org.tr/en/download/article-file/3261574 |
| work_keys_str_mv | AT javaidiqbalbhat unveilingthecomplexityofmedicalimagingthroughdeeplearningapproaches AT novsheenarasool unveilingthecomplexityofmedicalimagingthroughdeeplearningapproaches |