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Dr Mostafa Sarker


A black hole-aided deep-helix channel model for DNA. [Preprint] (2022)
Working Paper
SARKER, M.A.L., KADER, M.F., SARKER, M.M.K., LEE, M.H. and HAN, D.S. 2022. A black hole-aided deep-helix channel model for DNA. Research square [online], 10 January 2022, Preprint (version 3). Available from: https://doi.org/10.21203/rs.3.rs-1026992/v3

In this article, we present a black-hole-aided deep-helix (bh-dh) channel model to enhance information bound and mitigate a multiple-helix directional issue in Deoxyribonucleic acid (DNA) communications. The recent observations of DNA do not match wi... Read More about A black hole-aided deep-helix channel model for DNA. [Preprint].

AWEU-Net: an attention-aware weight excitation U-Net for lung nodule segmentation. (2021)
Journal Article
BANU, S.F., SARKER, M.M.K., ABDEL-NASSER, M., PUIG, D. and RASWAN, H.A. 2021. AWEU-Net: an attention-aware weight excitation U-Net for lung nodule segmentation. Applied science [online], 11(21), article 10132. Available from: https://doi.org/10.3390/app112110132

Lung cancer is a deadly cancer that causes millions of deaths every year around the world. Accurate lung nodule detection and segmentation in computed tomography (CT) images is a vital step for diagnosing lung cancer early. Most existing systems face... Read More about AWEU-Net: an attention-aware weight excitation U-Net for lung nodule segmentation..

A means of assessing deep learning-based detection of ICOS protein expression in colon cancer. (2021)
Journal Article
SARKER, M.M.K., MAKHLOUF, Y., CRAIG, S.G., HUMPHRIES, M.P., LOUGHREY, M., JAMES, J.A., SALTO-TELLEZ, M., O'REILLY, P. and MAXWELL, P. 2021. A means of assessing deep learning-based detection of ICOS protein expression in colon cancer. Cancers [online], 13(15): machine learning techniques in cancer, article 3825. Available from: https://doi.org/10.3390/cancers13153825

Biomarkers identify patient response to therapy. The potential immune‐checkpoint bi-omarker, Inducible T‐cell COStimulator (ICOS), expressed on regulating T‐cell activation and involved in adaptive immune responses, is of great interest. We have prev... Read More about A means of assessing deep learning-based detection of ICOS protein expression in colon cancer..

SLSNet: skin lesion segmentation using a lightweight generative adversarial network. (2021)
Journal Article
SARKER, M.M.K., RASHWAN, H.A., AKRAM, F., SINGH, V.K., BANU, S.F., CHOWDHURY, F.U.H., CHOUDHURY, K.A., CHAMBON, S., RADEVA, P., PUIG, D. and ABDEL-NASSER, M. 2021. SLSNet: skin lesion segmentation using a lightweight generative adversarial network. Expert systems with applications [online], 183, article 115433. Available from: https://doi.org/10.1016/j.eswa.2021.115433

The determination of precise skin lesion boundaries in dermoscopic images using automated methods faces many challenges, most importantly, the presence of hair, inconspicuous lesion edges and low contrast in dermoscopic images, and variability in the... Read More about SLSNet: skin lesion segmentation using a lightweight generative adversarial network..

Web‐based efficient dual attention networks to detect COVID‐19 from X‐ray images. (2020)
Journal Article
SARKER, M.M.K., MAKHLOUF, Y., BANU, S.F., CHAMBON, S., RADEVA, P. and PUIG, D. 2020. Web-based efficient dual attention networks to detect COVID-19 from X-ray images. Electronics letters [online], 56(24), pages 1298-1301. Available from: https://doi.org/10.1049/el.2020.1962

Rapid and accurate detection of COVID-19 is a crucial step to control the virus. For this purpose, the authors designed a web-based COVID-19 detector using efficient dual attention networks, called ‘EDANet’. The EDANet architecture is based on invert... Read More about Web‐based efficient dual attention networks to detect COVID‐19 from X‐ray images..

FinSeg: finger parts semantic segmentation using multi-scale feature maps aggregation of FCN. (2019)
Conference Proceeding
SALEH, A., RASHWAN, H., ABDEL-NASSER, M., SINGH, V., ABDULWAHAB, S., SARKER, M., GARCIA, M. and PUIG, D. 2019. FinSeg: finger parts semantic segmentation using multi-scale feature maps aggregation of FCN. In Tremeau, A., Farinella, G.M. and Braz, J. (eds.). Proceedings of 14th international joint conferences on Computer vision, imaging and computer graphics theory and applications 2019 (VISIGRAPP 2019), 25-27 February 2019, Prague, Czech Republic. Setúbal, Portugal: SciTePress [online], 5, pages 77-84. Available from: https://doi.org/10.5220/0007382100770084

Image semantic segmentation is in the center of interest for computer vision researchers. Indeed, huge number of applications requires efficient segmentation performance, such as activity recognition, navigation, and human body parsing, etc. One of t... Read More about FinSeg: finger parts semantic segmentation using multi-scale feature maps aggregation of FCN..

Conditional generative adversarial and convolutional networks for X-ray breast mass segmentation and shape classification. (2018)
Conference Proceeding
SINGH, V.K., ROMANI, S., RASHWAN, H.A., AKRAM, F., PANDEY, N., SARKER, M.M.K., ABDULWAHAB, S., TORRENTS-BARRENA, J., SALEH, A., ARQUEZ, M., ARENAS, M. and PUIG, D. 2018. Conditional generative adversarial and convolutional networks for X-ray breast mass segmentation and shape classification. In Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-López, C. and Fictinger, G. (eds.) Medical image computing and computer assisted intervention (MICCAI 2018): proceedings of 21st international conference on Medical image computing and computer assisted interventions 2018 (MICCAI 2018), 16-20 September 2018, Granada, Spain. Lecture notes in computer science, 11071. Cham: Springer [online], pages 833-840. Available from: https://doi.org/10.1007/978-3-030-00934-2_92

This paper proposes a novel approach based on conditional Generative Adversarial Networks (cGAN) for breast mass segmentation in mammography. We hypothesized that the cGAN structure is well-suited to accurately outline the mass area, especially when... Read More about Conditional generative adversarial and convolutional networks for X-ray breast mass segmentation and shape classification..

SLSDeep: Skin Lesion Segmentation Based on Dilated Residual and Pyramid Pooling Networks (2018)
Conference Proceeding
SARKER, M.M.K., RASHWAN, H.A., AKRAM, F., BANU, S.F., SALEH, A., SINGH, V.K., CHOWDHURY, F.U.H., ABDULWAHAB, S., ROMANI, S., RADEVA, P. and PUIG, D. 2018. SLSDeep: skin lesion segmentation based on dilated residual and pyramid pooling networks. In Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-López, C. and Fictinger, G. (eds.) Medical image computing and computer assisted intervention (MICCAI 2018): proceedings of 21st international conference on Medical image computing and computer assisted interventions 2018 (MICCAI 2018), 16-20 September 2018, Granada, Spain. Lecture notes in computer science, 11071. Cham: Springer [online], pages 21-29. Available from: https://doi.org/10.1007/978-3-030-00934-2_3

Skin lesion segmentation (SLS) in dermoscopic images is a crucial task for automated diagnosis of melanoma. In this paper, we present a robust deep learning SLS model represented as an encoder-decoder network. The encoder network is constructed by di... Read More about SLSDeep: Skin Lesion Segmentation Based on Dilated Residual and Pyramid Pooling Networks.

Deep visual embedding for image classification. (2018)
Conference Proceeding
SALEH, A., ABDEL-NASSER, M., SARKER, M.M.K., SINGH, V.K., ABDULWAHAB, S., SAFFARI, N., GARCIA, M.A. and PUIG, D. 2018. Deep visual embedding for image classification. In Proceedings of 2018 international conference on Innovative trends in computer engineering (ITCE 2018), 19-21 February 2018, Aswan, Egypt. Piscataway: IEEE [online], pages 31-35. Available from: https://doi.org/10.1109/ITCE.2018.8316596

This paper proposes a new visual embedding method for image classification. It goes further in the analogy with textual data and allows us to read visual sentences in a certain order as in the case of text. The proposed method considers the spatial r... Read More about Deep visual embedding for image classification..