Explainable AI for early malaria detection using stacked-LSTM and attention mechanisms
Malaria remains a global public health challenge, affecting more than 247 million people and causing 619,000 deaths worldwide in 2024 (according to WHO). Rapid diagnosis is essential for effective treatment and to improve patients’ chances of survival. In this study, we propose an interpretable deep...
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Elsevier
2025-01-01
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| Series: | Informatics in Medicine Unlocked |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352914825000553 |
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| author | Adil Gaouar Souaad Hamza Cherif Abdellatif Rahmoun Mostafa El Habib Daho |
| author_facet | Adil Gaouar Souaad Hamza Cherif Abdellatif Rahmoun Mostafa El Habib Daho |
| author_sort | Adil Gaouar |
| collection | DOAJ |
| description | Malaria remains a global public health challenge, affecting more than 247 million people and causing 619,000 deaths worldwide in 2024 (according to WHO). Rapid diagnosis is essential for effective treatment and to improve patients’ chances of survival. In this study, we propose an interpretable deep learning framework for accurate malaria diagnosis using blood smear images. Also, We evaluate and compare several baseline deep learning (DL) models (fundamentals), customized VGG-16 and VGG-19, as well as newer DL models such as Vision Transformer (ViT) and MobileNet, and, for the first time, a stacked long-short-term memory network (stacked-LSTM) with an attention mechanism for automatic detection of malaria from blood smear images. These models were trained and validated on a publicly available dataset of over 27.000 labeled blood smear images. The comparative and statistical study conducted in this research showed us that the proposed Stacked-LSTM model with attention mechanism outperformed all other approaches, achieving a classification accuracy (0.9912), sensitivity, specificity, precision, F1 score (0.9911), and area under the curve (AUC) superior to all other models. Despite their solid performance, these models are often considered ”black boxes” due to their lack of transparency in the decision-making process, which poses significant challenges in medical applications and fields where human life is at stake. To address this, we have integrated explainable AI (XAI) techniques, namely Grad-CAM and LIME, to improve the model’s interpretability. Our results demonstrate the complementary value of combining high-performance deep learning models with XAI methods to enhance trust and certainty in AI-assisted medical diagnosis, suggesting that our model can support early and interpretable malaria detection in clinical environments. |
| format | Article |
| id | doaj-art-3f6a42c85dd1443496e06c4a001b94e3 |
| institution | Matheson Library |
| issn | 2352-9148 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Informatics in Medicine Unlocked |
| spelling | doaj-art-3f6a42c85dd1443496e06c4a001b94e32025-07-17T04:44:36ZengElsevierInformatics in Medicine Unlocked2352-91482025-01-0157101667Explainable AI for early malaria detection using stacked-LSTM and attention mechanismsAdil Gaouar0Souaad Hamza Cherif1Abdellatif Rahmoun2Mostafa El Habib Daho3University Abou Bekr Belkaid, Tlemcen, Algeria; Corresponding author.University Abou Bekr Belkaid, Tlemcen, AlgeriaÉcole supérieure en informatique - Sidi Bel Abbès, AlgeriaLaTIM UMR 1101, Inserm, Brest, France; University of Western Brittany, Brest, France; Corresponding author at: University of Western Brittany, Brest, France.Malaria remains a global public health challenge, affecting more than 247 million people and causing 619,000 deaths worldwide in 2024 (according to WHO). Rapid diagnosis is essential for effective treatment and to improve patients’ chances of survival. In this study, we propose an interpretable deep learning framework for accurate malaria diagnosis using blood smear images. Also, We evaluate and compare several baseline deep learning (DL) models (fundamentals), customized VGG-16 and VGG-19, as well as newer DL models such as Vision Transformer (ViT) and MobileNet, and, for the first time, a stacked long-short-term memory network (stacked-LSTM) with an attention mechanism for automatic detection of malaria from blood smear images. These models were trained and validated on a publicly available dataset of over 27.000 labeled blood smear images. The comparative and statistical study conducted in this research showed us that the proposed Stacked-LSTM model with attention mechanism outperformed all other approaches, achieving a classification accuracy (0.9912), sensitivity, specificity, precision, F1 score (0.9911), and area under the curve (AUC) superior to all other models. Despite their solid performance, these models are often considered ”black boxes” due to their lack of transparency in the decision-making process, which poses significant challenges in medical applications and fields where human life is at stake. To address this, we have integrated explainable AI (XAI) techniques, namely Grad-CAM and LIME, to improve the model’s interpretability. Our results demonstrate the complementary value of combining high-performance deep learning models with XAI methods to enhance trust and certainty in AI-assisted medical diagnosis, suggesting that our model can support early and interpretable malaria detection in clinical environments.http://www.sciencedirect.com/science/article/pii/S2352914825000553Malaria detectionStacked-LSTMDeep learningExplainable AIGrad-CAMLIME |
| spellingShingle | Adil Gaouar Souaad Hamza Cherif Abdellatif Rahmoun Mostafa El Habib Daho Explainable AI for early malaria detection using stacked-LSTM and attention mechanisms Informatics in Medicine Unlocked Malaria detection Stacked-LSTM Deep learning Explainable AI Grad-CAM LIME |
| title | Explainable AI for early malaria detection using stacked-LSTM and attention mechanisms |
| title_full | Explainable AI for early malaria detection using stacked-LSTM and attention mechanisms |
| title_fullStr | Explainable AI for early malaria detection using stacked-LSTM and attention mechanisms |
| title_full_unstemmed | Explainable AI for early malaria detection using stacked-LSTM and attention mechanisms |
| title_short | Explainable AI for early malaria detection using stacked-LSTM and attention mechanisms |
| title_sort | explainable ai for early malaria detection using stacked lstm and attention mechanisms |
| topic | Malaria detection Stacked-LSTM Deep learning Explainable AI Grad-CAM LIME |
| url | http://www.sciencedirect.com/science/article/pii/S2352914825000553 |
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