Lossless Compression of Malaria-Infected Erythrocyte Images Using Vision Transformer and Deep Autoencoders
Lossless compression of medical images allows for rapid image data exchange and faithful recovery of the compressed data for medical image assessment. There are many useful telemedicine applications, for example in diagnosing conditions such as malaria in resource-limited regions. This paper present...
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| Format: | Article |
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MDPI AG
2025-04-01
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| Series: | Computers |
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| Online Access: | https://www.mdpi.com/2073-431X/14/4/127 |
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| author | Md Firoz Mahmud Zerin Nusrat W. David Pan |
| author_facet | Md Firoz Mahmud Zerin Nusrat W. David Pan |
| author_sort | Md Firoz Mahmud |
| collection | DOAJ |
| description | Lossless compression of medical images allows for rapid image data exchange and faithful recovery of the compressed data for medical image assessment. There are many useful telemedicine applications, for example in diagnosing conditions such as malaria in resource-limited regions. This paper presents a novel machine learning-based approach where lossless compression of malaria-infected erythrocyte images is assisted by cutting-edge classifiers. To this end, we first use a Vision Transformer to classify images into two categories: those cells that are infected with malaria and those that are not. We then employ distinct deep autoencoders for each category, which not only reduces the dimensions of the image data but also preserves crucial diagnostic information. To ensure no loss in reconstructed image quality, we further compress the residuals produced by these autoencoders using the Huffman code. Simulation results show that the proposed method achieves lower overall bit rates and thus higher compression ratios than traditional compression schemes such as JPEG 2000, JPEG-LS, and CALIC. This strategy holds significant potential for effective telemedicine applications and can improve diagnostic capabilities in regions impacted by malaria. |
| format | Article |
| id | doaj-art-c5d7faa3877141b1bd77adff677f963f |
| institution | DOAJ |
| issn | 2073-431X |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Computers |
| spelling | doaj-art-c5d7faa3877141b1bd77adff677f963f2025-08-20T03:13:54ZengMDPI AGComputers2073-431X2025-04-0114412710.3390/computers14040127Lossless Compression of Malaria-Infected Erythrocyte Images Using Vision Transformer and Deep AutoencodersMd Firoz Mahmud0Zerin Nusrat1W. David Pan2Department of Electrical and Computer Engineering, University of Alabama in Huntsville, Huntsville, AL 35899, USADepartment of Electrical and Computer Engineering, University of Alabama in Huntsville, Huntsville, AL 35899, USADepartment of Electrical and Computer Engineering, University of Alabama in Huntsville, Huntsville, AL 35899, USALossless compression of medical images allows for rapid image data exchange and faithful recovery of the compressed data for medical image assessment. There are many useful telemedicine applications, for example in diagnosing conditions such as malaria in resource-limited regions. This paper presents a novel machine learning-based approach where lossless compression of malaria-infected erythrocyte images is assisted by cutting-edge classifiers. To this end, we first use a Vision Transformer to classify images into two categories: those cells that are infected with malaria and those that are not. We then employ distinct deep autoencoders for each category, which not only reduces the dimensions of the image data but also preserves crucial diagnostic information. To ensure no loss in reconstructed image quality, we further compress the residuals produced by these autoencoders using the Huffman code. Simulation results show that the proposed method achieves lower overall bit rates and thus higher compression ratios than traditional compression schemes such as JPEG 2000, JPEG-LS, and CALIC. This strategy holds significant potential for effective telemedicine applications and can improve diagnostic capabilities in regions impacted by malaria.https://www.mdpi.com/2073-431X/14/4/127lossless compressiondeep autoencodersdeep learningmalariavision transformerHuffman encoding |
| spellingShingle | Md Firoz Mahmud Zerin Nusrat W. David Pan Lossless Compression of Malaria-Infected Erythrocyte Images Using Vision Transformer and Deep Autoencoders Computers lossless compression deep autoencoders deep learning malaria vision transformer Huffman encoding |
| title | Lossless Compression of Malaria-Infected Erythrocyte Images Using Vision Transformer and Deep Autoencoders |
| title_full | Lossless Compression of Malaria-Infected Erythrocyte Images Using Vision Transformer and Deep Autoencoders |
| title_fullStr | Lossless Compression of Malaria-Infected Erythrocyte Images Using Vision Transformer and Deep Autoencoders |
| title_full_unstemmed | Lossless Compression of Malaria-Infected Erythrocyte Images Using Vision Transformer and Deep Autoencoders |
| title_short | Lossless Compression of Malaria-Infected Erythrocyte Images Using Vision Transformer and Deep Autoencoders |
| title_sort | lossless compression of malaria infected erythrocyte images using vision transformer and deep autoencoders |
| topic | lossless compression deep autoencoders deep learning malaria vision transformer Huffman encoding |
| url | https://www.mdpi.com/2073-431X/14/4/127 |
| work_keys_str_mv | AT mdfirozmahmud losslesscompressionofmalariainfectederythrocyteimagesusingvisiontransformeranddeepautoencoders AT zerinnusrat losslesscompressionofmalariainfectederythrocyteimagesusingvisiontransformeranddeepautoencoders AT wdavidpan losslesscompressionofmalariainfectederythrocyteimagesusingvisiontransformeranddeepautoencoders |