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The proposed attention module facilitates the base network to automatically focus on the salient features arising due to the macular structural abnormalities while suppressing the irrelevant (or no cues) regions. The superiority of our proposed method lies in the fact that it does not require any pre-processing steps such as retinal flattening, denoising, and selection of a region of interest making it fully automatic and end-to-end trainable. Additionally, it requires a reduced number of network model parameters while achieving higher diagnostic performance. Extensive experimental results, analysis on four datasets along with the ablation studies show that the proposed architecture achieves state-of-the-art performance.<\/jats:p>","DOI":"10.1007\/s42979-022-01024-0","type":"journal-article","created":{"date-parts":[[2022,1,22]],"date-time":"2022-01-22T09:02:49Z","timestamp":1642842169000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["MacularNet: Towards Fully Automated Attention-Based Deep CNN for Macular Disease Classification"],"prefix":"10.1007","volume":"3","author":[{"given":"Sapna S.","family":"Mishra","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8417-1410","authenticated-orcid":false,"given":"Bappaditya","family":"Mandal","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Niladri B.","family":"Puhan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,1,22]]},"reference":[{"issue":"3","key":"1024_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s42979-021-00576-x","volume":"2","author":"A Aghaei","year":"2021","unstructured":"Aghaei A, Nazari A, Moghaddam ME. 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