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Publication Metadata only An efficient end-to-end deep neural network for interstitial lung disease recognition and classification(Tubitak Scientific & Technological Research Council Turkey, 2022) Junayed, Masum Shah; Jeny, Afsana Ahsan; Islam, Md Baharul; Ahmed, Ikhtiar; Shah, Afm Shahen; Bahcesehir University; Daffodil International University; Dortmund University of Technology; Yildiz Technical UniversityThe automated Interstitial Lung Diseases (ILDs) classification technique is essential for assisting clinicians during the diagnosis process. Detecting and classifying ILDs patterns is a challenging problem. This paper introduces an end-to-end deep convolution neural network (CNN) for classifying ILDs patterns. The proposed model comprises four convolutional layers with different kernel sizes and Rectified Linear Unit (ReLU) activation function, followed by batch normalization and max-pooling with a size equal to the final feature map size well as four dense layers. We used the ADAM optimizer to minimize categorical cross-entropy. A dataset consisting of 21328 image patches of 128 CT scans with five classes is taken to train and assess the proposed model. A comparison study showed that the presented model outperformed pre-trained CNNs and five-fold cross-validation on the same dataset. For ILDs pattern classification, the proposed approach achieved the accuracy scores of 99.09% and the average F score of 97.9% that outperforms three pre-trained CNNs. These outcomes show that the proposed model is relatively state-of-the-art in precision, recall, f score, and accuracy.Publication Metadata only SkNet: A Convolutional Neural Networks Based Classification Approach for Skin Cancer Classes(IEEE, 2020) Jeny, Afsana Ahsan; Sakib, Abu Noman Md; Junayed, Masum Shah; Lima, Khadija Akter; Ahmed, Ikhtiar; Islam, Md Baharul; Daffodil International University; Khulna University of Engineering & Technology (KUET); Bahcesehir University; Daffodil International UniversitySkin Cancer is one of the most common types of cancer. A solution for this globally recognized health problem is much required. Machine Learning techniques have brought revolutionary changes in the field of biomedical researches. Previously, It took a significant amount of time and much effort in detecting skin cancers. In recent years, many works have been done with Deep Learning which made the process a lot faster and much more accurate. In this paper, We have proposed a novel Convolutional Neural Networks (CNN) based approach that can classify four different types of Skin Cancer. We have developed our model SkNet consisting of 19 convolution layers. In previous works, the highest accuracy gained on 1000 images was 80.52%. Our proposed model exceeded that previous performance and achieved an accuracy of 95.26% on a dataset of 4800 images which is the highest acquired accuracy.Publication Metadata only Improving Image Compression With Adjacent Attention and Refinement Block(IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2023) Jeny, Afsana Ahsan; Islam, Md Baharul; Junayed, Masum Shah; Das, Debashish; Daffodil International University; Bahcesehir University; Birmingham City UniversityRecently, learned image compression algorithms have shown incredible performance compared to classic hand-crafted image codecs. Despite its considerable achievements, the fundamental disadvantage is not optimized for retaining local redundancies, particularly non-repetitive patterns, which have a detrimental influence on the reconstruction quality. This paper introduces the autoencoder-style network-based efficient image compression method, which contains three novel blocks, i.e., adjacent attention block, Gaussian merge block, and decoded image refinement block, to improve the overall image compression performance. The adjacent attention block allocates the additional bits required to capture spatial correlations (both vertical and horizontal) and effectively remove worthless information. The Gaussian merge block assists the rate-distortion optimization performance, while the decoded image refinement block improves the defects in low-resolution reconstructed images. A comprehensive ablation study analyzes and evaluates the qualitative and quantitative capabilities of the proposed model. Experimental results on two publicly available datasets reveal that our method outperforms the state-of-the-art methods on the KODAK dataset (by around 4dB and 5dB) and CLIC dataset (by about 4dB and 3dB) in terms of PSNR and MS-SSIM.Publication Metadata only ScarNet: Development and Validation of a Novel Deep CNN Model for Acne Scar Classification With a New Dataset(IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2022) Junayed, Masum Shah; Islam, Md Baharul; Jeny, Afsana Ahsan; Sadeghzadeh, Arezoo; Biswas, Topu; Shah, A. F. M. Shahen; Daffodil International University; Bahcesehir University; Multimedia University; Yildiz Technical UniversityAcne scarring occurs in 95% of people with acne vulgaris due to collagen loss or gains when the body is healing the damages of the skin caused by acne inflammation. Accurate classification of acne scars is a vital factor in providing a timely, effective treatment protocol. Dermatologists mainly recognize the type of acne scars manually based on visual inspections, which are time- and energy-consuming and subject to intra- and inter-reader variability. In this paper, a novel automated acne scar classification system is proposed based on a deep Convolutional Neural Network (CNN) model. First, a dataset of 250 images from five different classes is collected and labeled by four well-experienced dermatologists. The pre-processed input images are fed into our proposed model, namely ScarNet, for deep feature map extraction. The optimizer, loss function, activation functions, filter and kernel sizes, regularization methods, and the batch size of the proposed architecture are tuned so that the classification performance is maximized while minimizing the computational cost. Experimental results demonstrate the feasibility of the proposed method with accuracy, specificity, and kappa score of 92.53%, 95.38%, and 76.7%, respectively.Publication Metadata only DEEP NEURAL NETWORK-BASED ENSEMBLE MODEL FOR EYE DISEASES DETECTION AND CLASSIFICATION(INT SOC STEREOLOGY, 2023) Jeny, Afsana Ahsan; Junayed, Masum Shah; Islam, Md Baharul; Bahcesehir University; University of Connecticut; Daffodil International UniversityFundus images are the principal tool for observing and recognizing a wide range of ophthalmological abnormalities. The automatic and robust methods based on color fundus images are urgently needed since few symptoms are observable in the early stages of the disease. Experts must manually evaluate images to detect diseases for screening procedures to be effective. Due to the complexity of the screening procedure and the shortage of experienced personnel, developing successful screening-based treatments is costly. Although existing automated approaches strive to address these issues, they cannot handle a wide range of diseases and real-world circumstances. We design an automated deep learning-based ensemble method to detect and classify eye diseases from fundus images to address the abovementioned problems. A deep CNN-based model is proposed in the ensemble method that incorporates a mix of 20 layers, including the activation, optimization, and loss functions. The contrast-limited adaptive histogram equalization (CLAHE) and Gaussian filter are utilized in the pre-processing step to get more explicit images and eliminate noise. To avoid overfitting in the training phase, augmentation techniques are applied. Three pre-trained CNN models, including VGG16, DenseNet201, and ResNet50, are employed to compare and assess the efficiency of the proposed CNN model. Experimental results demonstrate that the ensemble approach outperforms recent approaches, which is comparatively state-of-art in the ODIR publicly available dataset.Publication Metadata only Machine Vision-Based Expert System for Automated Skin Cancer Detection(SPRINGER INTERNATIONAL PUBLISHING AG, 2022) Junayed, Masum Shah; Jeny, Afsana Ahsan; Rada, Lavdie; Islam, Md Baharul; BritoLoeza, C; MartinGonzalez, A; CastanedaZeman, V; Safi, A; Bahcesehir University; Daffodil International UniversitySkin cancer is the most frequently occurring kind of cancer, accounting for about one-third of all cases. Automatic early detection without expert intervention for a visual inspection would be of great help for society. The image processing and machine learning methods have significantly contributed to medical and biomedical research, resulting in fast and exact inspection in different problems. One of such problems is accurate cancer detection and classification. In this study, we introduce an expert system based on image processing and machine learning for skin cancer detection and classification. The proposed approach consists of three significant steps: pre-processing, feature extraction, and classification. The pre-processing step uses the grayscale conversion, Gaussian filter, segmentation, and morphological operation to represent skin lesion images better. We employ two feature extractors, i.e., the ABCD scoring method (asymmetry, border, color, diameter) and gray level co-occurrence matrix (GLCM), to extract cancer-affected areas. Finally, five different machine learning classifiers such as logistic regression (LR), decision tree (DT), k-nearest neighbors (KNN), support vector machine (SVM), and random forest (RF) used to detect and classify skin cancer. Experimental results show that random forest exceeds all other classifiers achieving an accuracy of 97.62% and 0.97 Area Under Curve (AUC), which is state-of-the-art on the experimented open-source dataset PH2.Publication Metadata only Optimized video compression with residual split attention and swin-block artifact contraction(ACADEMIC PRESS INC ELSEVIER SCIENCE, 2023) Jeny, Afsana Ahsan; Islam, Md Baharul; Bahcesehir University; Daffodil International UniversityResearch in video compression has seen significant advancement in the last several years. However, the existing deep learning-based algorithms continue to be plagued by erroneous motion compression and ineffective motion compensation architectures, resulting in compression errors with a lower rate-distortion trade-off. To overcome these challenges, we present an end-to-end purely deep learning-based video compression method through a set of primary operations (e.g., motion estimation, motion compression, motion compensation, residual compression, and artifact contraction) differently. A deep residual attention split (DRAS) block is introduced for motion compression networks to pay more attention to certain image regions to create more effective features for the decoder while boosting the rate-distortion optimization (RDO) efficiency. A channel residual block (CRB) is proposed in motion compensation to yield a more accurate predicted frame, potentially improving the residual frame. To mitigate the compression errors, an artifact contraction module (ACM) by residual swin convolution UNet block is included in this model to improve the reconstruction quality. To improve the final frame, a buffer is added to fine-tune the previous reference frames. These modules combine with a loss function by assessing the trade-off and enhancing the decoded video quality. A comprehensive ablation study demonstrates the effectiveness of the proposed blocks and modules for video compression. Experimental results show the competitive performance of the proposed method on four benchmark datasets.Publication Metadata only AN EFFICIENT END-TO-END IMAGE COMPRESSION TRANSFORMER(IEEE, 2022) Jeny, Afsana Ahsan; Junayed, Masum Shah; Islam, Md Baharul; Bahcesehir UniversityImage and video compression received significant research attention and expanded their applications. Existing entropy estimation-based methods combine with hyperprior and local context, limiting their efficacy. This paper introduces an efficient end-to-end transformer-based image compression model, which generates a global receptive field to tackle the long-range correlation issues. A hyper encoder-decoder-based transformer block employs a multi-head spatial reduction self-attention (MHSRSA) layer to minimize the computational cost of the self-attention layer and enable rapid learning of multi-scale and high-resolution features. A Casual Global Anticipation Module (CGAM) is designed to construct highly informative adjacent contexts utilizing channel-wise linkages and identify global reference points in the latent space for end-to-end rate-distortion optimization (RDO). Experimental results demonstrate the effectiveness and competitive performance of the KODAK dataset.Publication Metadata only DeepPyNet: A Deep Feature Pyramid Network for Optical Flow Estimation(IEEE, 2021) Jeny, Afsana Ahsan; Islam, Md Baharul; Aydin, Tarkan; Cree, MJ; Bahcesehir UniversityRecent advances in optical flow prediction have been made possible by using feature pyramids and iterative refining. Though downsampling in feature pyramids may cause foreground items to merge with the background, the iterative processing could be incorrect in optical flow experiments. Particularly the outcomes of the movement of narrow and tiny objects can be more invisible in the flow scene. We introduce a novel method called DeepPyNet for optical flow estimation that includes feature extractor, multi-channel cost volume, and flow decoder. In this method, we propose a deep recurrent feature pyramid-based network for the end-to-end optical flow estimation. The feature extraction from each pixel of the feature map keeps essential information without modifying the feature receptive field. Then, a multi-scale 4 Dc orrelation volume is built from the visual similarity of each pair of pixels. Finally, we utilize the multi-scale correlation volumes to continuously update the flow field through an iterative recurrent method. Experimental results demonstrate that DeepPyNet significantly eliminates flow errors and provides state-of-the-art performance in various datasets. Moreover, DeepPyNet is less complex and uses only 6.1M parameters 81% and 35% smaller than the popular FlowNet and PWC-Net+, respectively.Publication Metadata only PoseTED: A Novel Regression-Based Technique for Recognizing Multiple Pose Instances(SPRINGER INTERNATIONAL PUBLISHING AG, 2021) Jeny, Afsana Ahsan; Junayed, Masum Shah; Islam, Md Baharul; Bebis, G; Athitsos, V; Yan, T; Lau, M; Li, F; Shi, C; Yuan, X; Mousas, C; Bruder, G; Bahcesehir UniversityPose estimation for multiple people can be viewed as a hierarchical set predicting challenge. Algorithms are needed to classify all persons according to their physical components appropriately. Pose estimation methods are divided into two categories: (1) heatmap-based, (2) regression-based. Heatmap-based techniques are susceptible to various heuristic designs and are not end-to-end trainable, while regression-based methods involve fewer intermediary non-differentiable stages. This paper presents a novel regression-based multi-instance human pose recognition network called PoseTED. It utilizes the well-known object detector YOLOv4 for person detection, and the spatial transformer network (STN) used as a cropping filter. After that, we used a CNN-based backbone that extracts deep features and positional encoding with an encoder-decoder transformer applied for keypoint detection, solving the heuristic design problem before regression-based techniques and increasing overall performance. A prediction-based feed-forward network (FFN) is used to predict several key locations' posture as a group and display the body components as an output. Two available public datasets are tested in this experiment. Experimental results are shown on the COCO andMPII datasets, with an average precision (AP) of 73.7% on the COCO val. dataset, 72.7% on the COCO test dev. dataset, and 89.7% on the MPII datasets, respectively. These results are comparable to the state-of-the-art methods.
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