Shandilya Gunjan, Gupta Sheifali, Bharany Salil, Rehman Ateeq Ur, Kaur Upinder, Som Hafizan Mat, Hussen Seada
Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, Punjab, India.
School of Computing, Gachon University, Seongnam-si, 13120, Republic of Korea.
BMC Med Inform Decis Mak. 2024 Dec 30;24(1):414. doi: 10.1186/s12911-024-02840-5.
Major underlying health issues can be indicated by even minor nail infections. Subungual Melanoma is one of the most severe kinds since it is identified at a much later stage than other conditions. The purpose of this research is to offer novel deep-learning algorithms that target the autonomous categorization of six forms of nail disorders by employing images: Blue Finger, Clubbing, Pitting, Onychogryphosis, Acral Lentiginous Melanoma, and Normal Nail or Healthy Nail Appearance. Based on this, we build an initial baseline CNN model, which is then further advanced by the introduction of the Hybrid Capsule CNN model by the reduction of space hierarchy deficiencies of the classic CNN model. All these models were trained and tested using the Nail Disease Detection dataset with intensive uses of techniques of data augmentation. The Hybrid Capsule CNN model, thus, provided superior classification accuracy compared to the others; the training accuracy was 99.40%, while the validation accuracy was 99.25%, whereas the hybrid model outperformed the Base CNN model with astounding precision, recall of 97.35% and 96.79%. The hybrid model additionally leverages the capsule network and dynamic routing, offering improved robustness against transformations as well as improving spatial properties. The current study consequently provides a very viable, economical, and accessible diagnostic tool, especially for places with a paucity of medical services. The proposed methodology provides tremendous capacity for early diagnosis and better outcomes for the patient in a healthcare scenario. Clinical trial number Not applicable.
即使是轻微的指甲感染也可能预示着严重的潜在健康问题。甲下黑色素瘤是最严重的类型之一，因为它比其他病症在更晚的阶段才被发现。本研究的目的是提供新颖的深度学习算法，通过使用图像针对六种指甲病症进行自动分类：蓝指、杵状指、凹点、甲癣、肢端雀斑样黑色素瘤以及正常指甲或健康指甲外观。基于此，我们构建了一个初始基线卷积神经网络（CNN）模型，然后通过引入混合胶囊CNN模型来进一步改进，以减少经典CNN模型的空间层次缺陷。所有这些模型都使用指甲疾病检测数据集进行训练和测试，并大量使用了数据增强技术。因此，混合胶囊CNN模型相比其他模型提供了更高的分类准确率；训练准确率为99.40%，验证准确率为99.25%，而该混合模型以惊人的精度超过了基础CNN模型，召回率分别为97.35%和96.79%。该混合模型还利用了胶囊网络和动态路由，提高了对变换的鲁棒性并改善了空间特性。因此，本研究提供了一种非常可行、经济且易于使用的诊断工具，特别是对于医疗服务匮乏的地区。所提出的方法在医疗保健场景中为早期诊断和患者获得更好的治疗结果提供了巨大的能力。临床试验编号：不适用。
