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Muhammad Arsalan

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4 papers

1 author row

EAAI Journal 2024 Journal Article

Multiscale triplet spatial information fusion-based deep learning method to detect retinal pigment signs with fundus images

Muhammad Arsalan
Adnan Haider
Chanhum Park
Jin Seong Hong
Kang Ryoung Park

Inherited retinal diseases (IRDs) are genetic disorders that cause progressive deterioration of the photoreceptors associated with vision loss or blindness. Retinitis pigmentosa (RP) is a rare hereditary ophthalmic disease that initially causes night blindness owing to continuous retinal pigment deterioration. A computer-aided diagnosis (CAD)-based RP diagnosis solution by pigment sign detection can help ophthalmologists to analyze and treat the disease timely. At present, most of the research addresses retinal disease CAD using expensive optical coherence tomography (OCT); however, fundus imaging-based solutions are quick, convenient, and inexpensive for massive screening. This study proposes two convolutional neural networks (CNNs)-based segmentation that combines multiscale features by spatial information fusion: a single spatial fusion network (SSF-Net) and a triplet spatial fusion network (TSF-Net). SSF-Net fuses four multiscale spatial information streams. TSF-Net exploits triplet spatial information fusion by early, intermediate, and late fusion to ensure the fine segmentation of retinal pigment signs without preprocessing. TSF-Net creates a valuable difference in performance over SSF-Net. To evaluate SSF-Net and TSF-Net, the open dataset, named Retinal Images for Pigment Signs is utilized with 4-fold cross-validation. The experiment results confirm that SSF-Net and TSF-Net demonstrate superior performance compared to the state-of-the-art methods for the screening and analysis of RP disease.

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EAAI Journal 2023 Journal Article

Multi-scale feature retention and aggregation for colorectal cancer diagnosis using gastrointestinal images

Adnan Haider
Muhammad Arsalan
Se Hyun Nam
Jin Seong Hong
Haseeb Sultan
Kang Ryoung Park

Colonoscopy is considered the gold standard for colorectal cancer diagnosis and prognosis. However, existing methods are less accurate and prone to overlooking lesions during gastrointestinal endoscopic examinations. Computer-assisted diagnosis combined with robot-assisted minimally invasive surgery (RMIS) can significantly help medical practitioners detect and treat lesions. Therefore, two novel architectures are developed for polyp and surgical instrument segmentation to aid colorectal cancer diagnosis, assessment, and treatment. Colorectal cancer segmentation network (CCS-Net) is the base network used in this study. It uses the maximum convolutional layers near the input image for effective feature extraction from low-level information. In addition, CCS-Net uses an efficient feature upsampling unit to efficiently increase the input spatial features’ map size. Hence, CCS-Net is capable of providing a fair performance with satisfactory computational efficiency The multi-scale feature retention and aggregation network (MFRA-Net) is the final network in this study. MFRA-Net is developed to improve the segmentation accuracy of the CCS-Net further as it uses multi-scale feature retention to retain low-level spatial features and transfers them to deep stages of the network. MFRA-Net also combines multi-scale high-strided low-level information with high-level information to boost network segmentation performance. Finally, all the transferred multi-scale features from the early stages of the network are aggregated with high-level features in the deep levels of the network. This multi-scale feature retention and aggregation mechanism enables the network to maintain a better segmentation performance compared with other methods even with challenging blur, specular reflection, low contrast, and high variation cases. We evaluated both architectures on four challenging datasets: Kvasir-SEG, CVC-ClinicDB, Kvasir-Instrument, and the UW-Sinus-Surgery-Live dataset. The proposed method achieves dice similarity coefficients of 95. 98%, 94. 19%, 92. 81%, and 88. 57% for the CVC-ClinicDB, Kvasir-SEG, Kvasir-Instrument, and UW-Sinus-Surgery-Live datasets. The proposed method achieves superior segmentation performance compared with state-of-the-art methods and requires only 4. 9 million trainable parameters for complete training. Therefore, the proposed networks can effectively assist health professionals in surgical procedures and colorectal cancer diagnosis through surgical instruments and polyp segmentation, respectively.

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EAAI Journal 2023 Journal Article

Simple and robust depth-wise cascaded network for polyp segmentation

Tariq M. Khan
Muhammad Arsalan
Imran Razzak
Erik Meijering

The segmentation of the polyp region in colonoscopy images is considered difficult due to size, texture, and color variation. To segment polyps successfully, models based on convolutional neural networks (CNN), transformers, and their combinations have been developed. However, these methods are limited in that they can only model the local appearance of polyps or lack multi-level feature representation for spatial dependency in the decoding process. In this paper, we propose a simple, efficient yet powerful polyp segmentation framework that unifies the network with a multiscale cascaded path. The proposed MMS-Net utilizes multiscale and multipath convolutional operations in conjunction with multiple deep feature aggregation. The overall dense empowered features are sufficient for pixel-by-pixel detection of the polyp region. Extensive experiments on two popular benchmark datasets for polyp segmentation (Kvasir and CVC-Clinic DB) and two datasets of other medical applications (DRIVE and MC) are presented. The results show that our MMS-Net performs comparably to or better than other state-of-the-art methods despite having two or even three orders of magnitude fewer trainable parameters.

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JBHI Journal 2022 Journal Article

Deep Features Aggregation-Based Joint Segmentation of Cytoplasm and Nuclei in White Blood Cells

Adnan Haider
Muhammad Arsalan
Young Won Lee
Kang Ryoung Park

White blood cells (WBCs), also known as leukocytes, are one of the valuable parts of the blood and immune system. Typically, pathologists use microscope for the manual inspection of blood smears which is a time-consuming, error-prone, and labor-intensive procedure. To address these issues, we present two novel shallow networks: a leukocyte deep segmentation network (LDS-Net) and leukocyte deep aggregation segmentation network (LDAS-Net) for the joint segmentation of cytoplasm and nuclei in WBC images. LDS-Net is a shallow architecture with three downsampling stages and seven convolution layers. LDAS-Net is an extended version of LDS-Net that utilizes a novel pool-less low-level information transfer bridge to transfer low-level information to the deep layers of the network. This information is aggregated with deep features in a dense feature concatenation block to achieve accurate cytoplasm and nuclei joint segmentation. We evaluated our developed architectures on four WBC publicly available datasets. For cytoplasmic segmentation in WBCs, the proposed method achieved the dice coefficients of 98. 97%, 99. 0%, 96. 05%, and 98. 79% on Datasets 1, 2, 3, and 4, respectively. For nuclei segmentation, the dice coefficients of 96. 35% and 98. 09% are achieved for Datasets 1 and 2, respectively. Proposed method outperforms state-of-the-art methods with superior computational efficiency and requires only 6. 5 million trainable parameters.

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