Elangovan G, Kumar N Jagadish, Shobana J, Ramprasath M, Joshi Gyanendra Prasad, Cho Woong
Department of Data Science and Business Systems, School of Computing, SRM Institute of Science and Technology, Kattankulathur, Chennai, 603203, India.
Department of AI Software, Kangwon National University, Samcheok, 25913, Republic of Korea.
Sci Rep. 2024 Dec 28;14(1):31104. doi: 10.1038/s41598-024-82299-6.
Autism spectrum disorder (ASD) is a neurologic disorder considered to cause discrepancies in physical activities, social skills, and cognition. There is no specific medicine for treating this disorder; early intervention is critical to improving brain function. Additionally, the lack of a clinical test for detecting ASD makes diagnosis challenging. To regulate identification, physicians entertain the children's activities and growing histories. The human face is employed as a biological signature as it has the potential reflections of the brain. It is utilized as a simpler and more helpful tool for early detection. Artificial intelligence (AI) algorithms in medicinal rehabilitation and diagnosis can help specialists identify various illnesses more successfully. However, owing to its particular heterogeneous symptoms and complex nature, diagnosis of ASD remains to be challenging for investigators. This work presents a Fusion of Transfer Learning (TL) with the Dandelion Algorithm for Accurate Autism Spectrum Disorder Detection and Classification (FTLDA-AASDDC) method. The FTLDA-AASDDC technique detects and classifies autism and non-autism samples using facial images. To accomplish this, the FTLDA-AASDDC technique utilizes a bilateral filter (BF) approach for noise elimination. Next, the FTLDA-AASDDC technique employs a fusion-based TL process comprising three models, namely MobileNetV2, DenseNet201, and ResNet50. Moreover, the attention-based bi-directional long short-term memory (A-BiLSTM) method is used to classify and recognize ASD. Finally, the Dandelion Algorithm (DA) is employed to optimize the parameter tuning process, improving the efficacy of the A-BiLSTM technique. A wide range of simulation analyses is performed to highlight the ASD classification performance of the FTLDA-AASDDC technique. The experimental validation of the FTLDA-AASDDC technique portrayed a superior accuracy value of 97.50% over existing techniques.
自闭症谱系障碍（ASD）是一种神经疾病，被认为会导致身体活动、社交技能和认知方面的差异。目前尚无治疗该疾病的特效药物；早期干预对于改善脑功能至关重要。此外，缺乏用于检测ASD的临床测试使得诊断具有挑战性。为规范诊断，医生会了解儿童的活动情况和成长经历。人脸被用作一种生物特征，因为它能反映大脑的潜在状况。它被用作早期检测的一种更简单且更有用的工具。医学康复和诊断中的人工智能（AI）算法可以帮助专家更成功地识别各种疾病。然而，由于ASD具有特殊的异质性症状和复杂的性质，对于研究人员来说，ASD的诊断仍然具有挑战性。这项工作提出了一种将迁移学习（TL）与蒲公英算法融合的精确自闭症谱系障碍检测与分类方法（FTLDA - AASDDC）。FTLDA - AASDDC技术使用面部图像检测和分类自闭症和非自闭症样本。为此，FTLDA - AASDDC技术采用双边滤波器（BF）方法进行噪声消除。接下来，FTLDA - AASDDC技术采用基于融合的迁移学习过程，该过程包括三个模型，即MobileNetV2、DenseNet201和ResNet50。此外，基于注意力的双向长短期记忆（A - BiLSTM）方法用于对ASD进行分类和识别。最后，使用蒲公英算法（DA）来优化参数调整过程，提高A - BiLSTM技术的效率。进行了广泛的模拟分析以突出FTLDA - AASDDC技术的ASD分类性能。FTLDA - AASDDC技术的实验验证表明，其准确率高达97.50%，优于现有技术。
