عرض بسيط للتسجيلة

المؤلفPodder, Kanchon Kanti
المؤلفAlam, Mohammad Kaosar
المؤلفSiam, Zakaria Shams
المؤلفIslam, Khandaker Reajul
المؤلفDutta, Proma
المؤلفMushtak, Adam
المؤلفKhandakar, Amith
المؤلفPedersen, Shona
المؤلفChowdhury, Muhammad E.H.
تاريخ الإتاحة2023-11-14T07:12:42Z
تاريخ النشر2023-01-01
اسم المنشورStudies in Big Data
المعرّفhttp://dx.doi.org/10.1007/978-981-99-3784-4_6
الاقتباسPodder, K. K., Alam, M. K., Siam, Z. S., Islam, K. R., Dutta, P., Mushtak, A., ... & Chowdhury, M. E. (2023). Deep Learning-Based Conjunctival Melanoma Detection Using Ocular Surface Images. In Deep Learning Applications in Image Analysis (pp. 113-131). Singapore: Springer Nature Singapore.‏
الرقم المعياري الدولي للكتاب21976503
معرّف المصادر الموحدhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85166152410&origin=inward
معرّف المصادر الموحدhttp://hdl.handle.net/10576/49251
الملخصThe human eye could be affected with conjunctival melanoma, which indicates a fatal malignant growth of the eye. Being a very rare disease, there exists a lack of related data in the literature. Also, very few studies performed deep learning-based conjunctival melanoma detection from the ocular surface images. In response to the research gap, we created a more enriched and well-curated dataset validated by medical experts for conjunctival melanoma detection using deep learning. Furthermore, we have made our dataset available on Kaggle. In the present work, we utilized a total of seven pretrained deep learning-based classification models, that are, DenseNet161, DenseNet201, EfficientNet_B7, GoogLeNet, ResNet18, ResNet50, and ResNet152. We outperformed the prior literature in terms of assessment metrics, including different performance parameters like accuracy, precision, sensitivity, F1-score, specificity, confusion matrix, ROCcurve, and AUROC, with better improvement achieved in multi-label classification. The best AUROC and overall accuracy were found to be around 1.00 and 99.51%, respectively, for binary classification whereas these were around 0.99 and 94.42%, respectively, in case of multi-label classification, from the best performing model, EfficientNet_B7. The deep learning applications in medical images require visual interpretation of the features prioritized in decision-making, and a Grad-CAM-based visualization is added in this study to prove the effectiveness of the best-performing model. The research conducted here may make it simpler to identify conjunctival lesions using state-of-the-art deep learning models more meticulously in the future.
اللغةen
الناشرspringer link
الموضوعComputer-aided diagnosis
Conjunctival melanoma
Deep learning
Ocular surface images
Pretrained models
العنوانDeep Learning-Based Conjunctival Melanoma Detection Using Ocular Surface Images
النوعArticle
رقم المجلد129
dc.accessType Abstract Only


الملفات في هذه التسجيلة

الملفاتالحجمالصيغةالعرض

لا توجد ملفات لها صلة بهذه التسجيلة.

هذه التسجيلة تظهر في المجموعات التالية

عرض بسيط للتسجيلة