National Institute of Technical Teachers' Training & Research (Deemed to be University), Kolkata, 700106, India.
Kalinga Institute of Industrial Technology, Bhubaneswar, 751024, Orissa, India.
Sci Rep. 2024 Nov 4;14(1):26557. doi: 10.1038/s41598-024-76852-6.
Psoriasis, being a chronic, inflammatory, lifelong skin disorder, has become a major threat to the human population. The precise and effective diagnosis of psoriasis continues to be difficult for clinicians due to its varied nature. In northern India, the prevalence of psoriasis among adult population ranges from 0.44 to 2.8%. Chronic plaque psoriasis accounts for over 90% of cases. This study utilized a dataset of 325 raw images collected from a reputable local hospital using a digital camera under uniform lighting conditions. These images were processed to generate 496 image patches (both diseased and normal), which were then normalized and resized for model training. An automated psoriasis image recognition framework was developed using four state-of-the-art deep transfer learning models: VGG16, VGG19, MobileNetV1, and ResNet-50. The convolutional layers adopted various edge, shape, and color filters to generate the feature map for psoriasis detection. Each pre-trained model was adapted with two dense layers, one dropout layer, and one output layer to classify input images. Among these models, MobileNetV1 achieved the best performance, with 94.84% sensitivity, 89.37% specificity, and 97.24% overall accuracy. Hyper-parameter tuning was performed using grid search to optimize learning rates, batch sizes, and dropout rates. The AdaGrad (Adaptive gradient)) optimizer was chosen for its adaptive learning rate capabilities, facilitating quicker convergence in model performance. Consequently, the methodology's performance improved to 94.25% sensitivity, 96.42% specificity, and 99.13% overall accuracy. The model's performance was also compared with non-machine learning-based diagnostic methods, yielding a Dice coefficient of 0.98. However, the model's effectiveness is dependent upon high-quality input images, as poor image conditions may affect accuracy, and it may not generalize well across diverse demographics or psoriasis variations, highlighting the need for varied training datasets for robustness.
银屑病是一种慢性、炎症性、终身性皮肤疾病，已成为人类的主要威胁。由于其多变的性质，临床医生对银屑病的精确和有效诊断仍然具有挑战性。在印度北部，成年人口中银屑病的患病率在 0.44%至 2.8%之间。慢性斑块型银屑病占 90%以上。本研究利用从一家知名当地医院使用数码相机在均匀照明条件下收集的 325 张原始图像数据集。这些图像经过处理生成 496 个图像补丁（既有病变的也有正常的），然后对这些图像进行归一化和调整大小，以进行模型训练。使用四种最先进的深度迁移学习模型（VGG16、VGG19、MobileNetV1 和 ResNet-50）开发了一种自动银屑病图像识别框架。卷积层采用各种边缘、形状和颜色滤波器来生成用于银屑病检测的特征图。每个预训练模型都适应两个密集层、一个 dropout 层和一个输出层来对输入图像进行分类。在这些模型中，MobileNetV1 的性能最佳，敏感性为 94.84%，特异性为 89.37%，总准确率为 97.24%。使用网格搜索来优化学习率、批量大小和 dropout 率来进行超参数调整。选择 AdaGrad（自适应梯度）优化器是因为它具有自适应学习率的能力，有助于更快地提高模型性能。因此，该方法的性能提高到敏感性为 94.25%，特异性为 96.42%，总准确率为 99.13%。还将该模型的性能与基于非机器学习的诊断方法进行了比较，产生了 0.98 的骰子系数。然而，该模型的有效性取决于高质量的输入图像，因为较差的图像条件可能会影响准确性，并且它可能无法在不同的人群或银屑病变化中很好地推广，这突出了需要多样化的训练数据集以提高稳健性。
