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Such technology has urgent requirements for storage reduction and efficient use of the transmission channel bandwidth. Such requirements can be achieved efficiently via developing accurate medical image compression techniques. For accurate diagnosis, lossless compression methods are recommended. However, the tradeoff between the compression ratio (CR) and the preservation of the image quality is still challenging. On the other hand, the advantages of the convolutional neural networks inspired this work to design a novel proposed system for dermoscopic image compression based on the integration of the compression direction-ConvNet (CD-ConvNet) and decompression direction-ConvNet (DD-ConvNet) with discrete\u00a0cosine transform (DCT) and Huffman coding, called DermCompressNet. To reconstruct a high-quality image at the receiver, the inverse processes using the DD-ConvNet network were followed. The proposed system was evaluated by measuring several image quality metrics, namely the mean square error (MSE), peak signal-to-noise ratio (PSNR), and structural similarity index measure (SSIM), along with compression quality metrics, namely CR, and computational time (CT). The experimental results achieved 34.6 dB, 2.5, 0.85, and 56% of PSNR, MSE, SSIM, and CR, respectively. A comparison studies with the JPEG and state-of-the-art methods proved the superiority of the proposed system, showing23%, 16%, 4.3%, and 1.8% improvements in the PSNR, MSE, SSIM, and CR, respectively, compared to the JPEG.<\/jats:p>","DOI":"10.1007\/s11042-024-20069-2","type":"journal-article","created":{"date-parts":[[2024,9,21]],"date-time":"2024-09-21T05:01:41Z","timestamp":1726894901000},"page":"27747-27775","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["DermCompressNet: integrated CD-ConvNet and discrete cosine transform for dermoscopic images compression"],"prefix":"10.1007","volume":"84","author":[{"given":"Radwa A.","family":"Elsawy","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mohammed M.","family":"Abo-Zahhad","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4188-1268","authenticated-orcid":false,"given":"Maram A.","family":"Wahba","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Diaa-Eldin A.","family":"Mansour","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Amira S.","family":"Ashour","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,9,21]]},"reference":[{"key":"20069_CR1","doi-asserted-by":"publisher","first-page":"2250","DOI":"10.1109\/TMI.2017.2714640","volume":"36","author":"LFR Lucas","year":"2017","unstructured":"Lucas LFR, Rodrigues NMM, Da Silva Cruz LA, De Faria SMM (2017) Lossless Compression of Medical Images Using 3-D Predictors. 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