Sakib Mostafa

All Publications

• Feature transformation for improved software bug detection and commit classification JOURNAL OF SYSTEMS AND SOFTWARE Mostafa, S., Cynthia, S., Roy, B., Mondal, D. 2025; 219

  View details for DOI 10.1016/j.jss.2024.112205

  View details for Web of Science ID 001314144900001

• Explainable deep learning in plant phenotyping. Frontiers in artificial intelligence Mostafa, S., Mondal, D., Panjvani, K., Kochian, L., Stavness, I. 2023; 6: 1203546

  Abstract

  The increasing human population and variable weather conditions, due to climate change, pose a threat to the world's food security. To improve global food security, we need to provide breeders with tools to develop crop cultivars that are more resilient to extreme weather conditions and provide growers with tools to more effectively manage biotic and abiotic stresses in their crops. Plant phenotyping, the measurement of a plant's structural and functional characteristics, has the potential to inform, improve and accelerate both breeders' selections and growers' management decisions. To improve the speed, reliability and scale of plant phenotyping procedures, many researchers have adopted deep learning methods to estimate phenotypic information from images of plants and crops. Despite the successful results of these image-based phenotyping studies, the representations learned by deep learning models remain difficult to interpret, understand, and explain. For this reason, deep learning models are still considered to be black boxes. Explainable AI (XAI) is a promising approach for opening the deep learning model's black box and providing plant scientists with image-based phenotypic information that is interpretable and trustworthy. Although various fields of study have adopted XAI to advance their understanding of deep learning models, it has yet to be well-studied in the context of plant phenotyping research. In this review article, we reviewed existing XAI studies in plant shoot phenotyping, as well as related domains, to help plant researchers understand the benefits of XAI and make it easier for them to integrate XAI into their future studies. An elucidation of the representations within a deep learning model can help researchers explain the model's decisions, relate the features detected by the model to the underlying plant physiology, and enhance the trustworthiness of image-based phenotypic information used in food production systems.

  View details for DOI 10.3389/frai.2023.1203546

  View details for PubMedID 37795496

  View details for PubMedCentralID PMC10546035

• Leveraging Guided Backpropagation to Select Convolutional Neural Networks for Plant Classification. Frontiers in artificial intelligence Mostafa, S., Mondal, D., Beck, M. A., Bidinosti, C. P., Henry, C. J., Stavness, I. 2022; 5: 871162

  Abstract

  The development of state-of-the-art convolutional neural networks (CNN) has allowed researchers to perform plant classification tasks previously thought impossible and rely on human judgment. Researchers often develop complex CNN models to achieve better performances, introducing over-parameterization and forcing the model to overfit on a training dataset. The most popular process for evaluating overfitting in a deep learning model is using accuracy and loss curves. Train and loss curves may help understand the performance of a model but do not provide guidance on how the model could be modified to attain better performance. In this article, we analyzed the relation between the features learned by a model and its capacity and showed that a model with higher representational capacity might learn many subtle features that may negatively affect its performance. Next, we showed that the shallow layers of a deep learning model learn more diverse features than the ones learned by the deeper layers. Finally, we propose SSIM cut curve, a new way to select the depth of a CNN model by using the pairwise similarity matrix between the visualization of the features learned at different depths by using Guided Backpropagation. We showed that our proposed method could potentially pave a new way to select a better CNN model.

  View details for DOI 10.3389/frai.2022.871162

  View details for PubMedID 35647528

  View details for PubMedCentralID PMC9132261

• Diagnosis of Autism Spectrum Disorder Based on Functional Brain Networks with Deep Learning. Journal of computational biology : a journal of computational molecular cell biology Yin, W., Mostafa, S., Wu, F. X. 2021; 28 (2): 146-165

  Abstract

  Autism spectrum disorder (ASD) is a neurological and developmental disorder. Traditional diagnosis of ASD is typically performed through the observation of behaviors and interview of a patient. However, these diagnosis methods are time-consuming and can be misleading sometimes. Integrating machine learning algorithms with neuroimages, a diagnosis method, can possibly be established to detect ASD subjects from typical control subjects. In this study, we develop deep learning methods for diagnosis of ASD from functional brain networks constructed with brain functional magnetic resonance imaging (fMRI) data. The entire Autism Brain Imaging Data Exchange 1 (ABIDE 1) data set is utilized to investigate the performance of our proposed methods. First, we construct the brain networks from brain fMRI images and define the raw features based on such brain networks. Second, we employ an autoencoder (AE) to learn the advanced features from the raw features. Third, we train a deep neural network (DNN) with the advanced features, which achieves the classification accuracy of 76.2% and the receiving operating characteristic curve (AUC) of 79.7%. As a comparison, we also apply the same advanced features to train several traditional machine learning algorithms to benchmark the classification performance. Finally, we combine the DNN with the pretrained AE and train it with the raw features, which achieves the classification accuracy of 79.2% and the AUC of 82.4%. These results show that our proposed deep learning methods outperform the state-of-the-art methods.

  View details for DOI 10.1089/cmb.2020.0252

  View details for PubMedID 33074746

• Diagnosis of autism spectrum disorder with convolutional autoencoder and structural MRI images Neural Engineering Techniques for Autism Spectrum Disorder Mostafa, S., Wu, F. Elsevier. 2021: 23-38

  View details for DOI 10.1016/B978-0-12-822822-7.00003-X

• Visualizing Feature Maps for Model Selection in Convolutional Neural Networks Mostafa, S., Mondal, D., Beck, M., Bidinosti, C., Henry, C., Stavness, I., IEEE Comp Soc IEEE COMPUTER SOC. 2021: 1362-1371

  View details for DOI 10.1109/ICCVW54120.2021.00157

  View details for Web of Science ID 000739651101050

• Improving Deep Learning Classifiers by Learning Neuron Activation Patterns Mostafa, S., Yin, W., Wu, F. 2020: 39-51

  View details for DOI 10.1007/978-3-030-46165-2_4

• A network clustering based feature selection strategy for classifying autism spectrum disorder. BMC medical genomics Tang, L., Mostafa, S., Liao, B., Wu, F. X. 2019; 12 (Suppl 7): 153

  Abstract

  Advanced non-invasive neuroimaging techniques offer new approaches to study functions and structures of human brains. Whole-brain functional networks obtained from resting state functional magnetic resonance imaging has been widely used to study brain diseases like autism spectrum disorder (ASD). Auto-classification of ASD has become an important issue. Existing classification methods for ASD are based on features extracted from the whole-brain functional networks, which may be not discriminant enough for good performance.In this study, we propose a network clustering based feature selection strategy for classifying ASD. In our proposed method, we first apply symmetric non-negative matrix factorization to divide brain networks into four modules. Then we extract features from one of four modules called default mode network (DMN) and use them to train several classifiers for ASD classification.The computational experiments show that our proposed method achieves better performances than those trained with features extracted from the whole brain network.It is a good strategy to train the classifiers for ASD based on features from the default mode subnetwork.

  View details for DOI 10.1186/s12920-019-0598-0

  View details for PubMedID 31888621

  View details for PubMedCentralID PMC6936069

• Diagnosis of Autism Spectrum Disorder Based on Eigenvalues of Brain Networks IEEE ACCESS Mostafa, S., Tang, L., Wu, F. 2019; 7: 128474-128486

  View details for DOI 10.1109/ACCESS.2019.2940198

  View details for Web of Science ID 000487233800024