Examinando por Autor "Al-Asadi, Ahmed"
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Ítem Novel Transfer Learning Approach for Medical Imaging with Limited Labeled Data(MDPI (Switzerland), 2021-03-30) Alzubaidi, Laith; Al-Amidie, Muthana; Al-Asadi, Ahmed; Humaidi, Amjad J.; Al-Shamma, Omrad; Fadhel, Mohammed A.; Zhang, Jinglan; Santamaria, José; Duan, YeDeep learning requires a large amount of data to perform well. However, the field of medical image analysis suffers from a lack of sufficient data for training deep learning models. Moreover, medical images require manual labeling, usually provided by human annotators coming from various backgrounds. More importantly, the annotation process is time-consuming, expensive, and prone to errors. Transfer learning was introduced to reduce the need for the annotation process by transferring the deep learning models with knowledge from a previous task and then by fine-tuning them on a relatively small dataset of the current task. Most of the methods of medical image classification employ transfer learning from pretrained models, e.g., ImageNet, which has been proven to be ineffective. This is due to the mismatch in learned features between the natural image, e.g., ImageNet, and medical images. Additionally, it results in the utilization of deeply elaborated models. In this paper, we propose a novel transfer learning approach to overcome the previous drawbacks by means of training the deep learning model on large unlabeled medical image datasets and by next transferring the knowledge to train the deep learning model on the small amount of labeled medical images. Additionally, we propose a new deep convolutional neural network (DCNN) model that combines recent advancements in the field. We conducted several experiments on two challenging medical imaging scenarios dealing with skin and breast cancer classification tasks. According to the reported results, it has been empirically proven that the proposed approach can significantly improve the performance of both classification scenarios. In terms of skin cancer, the proposed model achieved an F1-score value of 89.09% when trained from scratch and 98.53% with the proposed approach. Secondly, it achieved an accuracy value of 85.29% and 97.51%, respectively, when trained from scratch and using the proposed approach in the case of the breast cancer scenario. Finally, we concluded that our method can possibly be applied to many medical imaging problems in which a substantial amount of unlabeled image data is available and the labeled image data is limited. Moreover, it can be utilized to improve the performance of medical imaging tasks in the same domain. To do so, we used the pretrained skin cancer model to train on feet skin to classify them into two classes—either normal or abnormal (diabetic foot ulcer (DFU)). It achieved an F1-score value of 86.0% when trained from scratch, 96.25% using transfer learning, and 99.25% using double-transfer learning.Ítem Towards Risk-Free Trustworthy Artificial Intelligence: Significance and Requirements(Wiley, Hindawi, 2023-10-26) Alzubaidi, Laith; Al-Sabaawi, Aiman; Bai, Jinshuai; Dukhan, Ammar; Alkenani, Ahmed H.; Al-Asadi, Ahmed; Alwzwazy, Haider A.; Manoufali, Mohammed; Fadhel, Mohammed A.; Albahri, Ahmed Shihab; Moreira, Catarina; Ouyang, Chun; Zhang, Jinglan; Santamaria, José; Salhi, Asma; Hollman, Freek; Gupta, Ashish; Duan, Ye; Rabczuk, Timon; Abbosh, Amin; Gu, YuantongGiven the tremendous potential and infuence of artifcial intelligence (AI) and algorithmic decision-making (DM), these systems have found wide-ranging applications across diverse felds, including education, business, healthcare industries, government, and justice sectors. While AI and DM ofer signifcant benefts, they also carry the risk of unfavourable outcomes for users and society. As a result, ensuring the safety, reliability, and trustworthiness of these systems becomes crucial. Tis article aims to provide a comprehensive review of the synergy between AI and DM, focussing on the importance of trustworthiness. Te review addresses the following four key questions, guiding readers towards a deeper understanding of this topic: (i) why do we need trustworthy AI? (ii) what are the requirements for trustworthy AI? In line with this second question, the key requirements that establish the trustworthiness of these systems have been explained, including explainability, accountability, robustness, fairness, acceptance of AI, privacy, accuracy, reproducibility, and human agency, and oversight. (iii) how can we have trustworthy data? and (iv) what are the priorities in terms of trustworthy requirements for challenging applications? Regarding this last question, six diferent applications have been discussed, including trustworthy AI in education, environmental science, 5G-based IoTnetworks, robotics for architecture, engineering and construction, fnancial technology, and healthcare. Te review emphasises the need to address trustworthiness in AI systems before their deployment in order to achieve the AI goal for good. An example is provided that demonstrates how trustworthy AI can be employed to eliminate bias in human resources management systems. Te insights and recommendations presented in this paper will serve as a valuable guide for AI researchers seeking to achieve trustworthiness in their applications.