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However, these images are susceptible to different types of noise, and it is hard to remove these noises due to their complex distribution. The presence of such noise significantly deteriorates the quality of the images and significantly affects the diagnosis performance. Hence, the design of an effective de-noising technique is highly essential to remove the noise from chest CT and X-ray images prior to further processing. Deep learning methods, mainly, CNN have shown tremendous progress on de-noising tasks. However, existing CNN based models estimate the noise from the final layers, which may not carry adequate details of the image. To tackle this issue, in this paper a deep multi-level semantic fusion network is proposed, called DMF-Net for the removal of noise from chest CT and X-ray images. The DMF-Net mainly comprises of a dilated convolutional feature extraction block, a cascaded feature learning block (CFLB) and a noise fusion block (NFB) followed by a prominent feature extraction block. The CFLB cascades the features from different levels (convolutional layers) which are later fed to NFB to attain correct noise prediction. Finally, the Prominent Feature Extraction Block(PFEB) produces the clean image. To validate the proposed de-noising technique, a separate and a mixed dataset containing high-resolution CT and X-ray images with specific and blind noise are used. Experimental results indicate the effectiveness of the DMF-Net compared to other state-of-the-art methods in the context of peak signal-to-noise ratio (PSNR) and structural similarity measurement (SSIM) while drastically cutting down on the processing power needed.<\/jats:p>","DOI":"10.1186\/s12880-023-01108-0","type":"journal-article","created":{"date-parts":[[2023,10,9]],"date-time":"2023-10-09T11:03:07Z","timestamp":1696849387000},"update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["DMF-Net: a deep multi-level semantic fusion network for high-resolution chest CT and X-ray image de-noising"],"prefix":"10.1186","volume":"23","author":[{"given":"Tapan Kumar","family":"Nayak","sequence":"first","affiliation":[]},{"given":"Chandra Sekhara Rao","family":"Annavarappu","sequence":"additional","affiliation":[]},{"given":"Soumya Ranjan","family":"Nayak","sequence":"additional","affiliation":[]},{"given":"Berihun Molla","family":"Gedefaw","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,10,9]]},"reference":[{"key":"1108_CR1","doi-asserted-by":"publisher","first-page":"73","DOI":"10.1016\/j.bspc.2018.01.010","volume":"42","author":"M Diwakar","year":"2018","unstructured":"Diwakar M, Kumar M. 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We declare that this paper is original and has been read and approved by all named authors and that there are no other persons who satisfied the criteria for authorship but are not listed. We further confirm that all have approved the order of authors listed in the paper of us. All the patients\u2019 shared data have been approved by the Ethical Committee of the Public Hospital of the Government Employees of Sao Paulo (HSPM), Sao Paulo\/Brazil and the same has been cited in the reference section of this paper. However, due to ethical concerns about patients\u2019 privacy information, the Kaggle database presented the dataset in TIFF format files. The same tiff file has been used in this current research experimentation. The study reported in this manuscript doesn\u2019t require any involving human participants, human data, or human tissue.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"150"}}