Abstract
Machine learning provides a plethora of approaches for tackling categorization problems, i.e., determining whether or not a data item belongs to a particular class. When the objective is to accurately classify new and unknown data, neural networks are frequently an excellent choice. The widespread availability of growing processing power, along with the development of more effective training algorithms, has enabled the application of deep learning principles. To provide superior learning solutions, deep architectures leverage recent breakthroughs in artificial intelligence and insights from cognitive neuroscience. Convolutional neural networks (CNNs) are a subclass of discriminative deep architectures that have demonstrated acceptable performance when processing 2D data with grid-like topologies, such as photos and videos. In this paper, we compare the performance of deep CNNs and machine learning for diabetic foot disease categorization. Diverse machine learning approaches were implemented for classification, viz., decision tree (DT), support vector machine (SVM), quadratic discriminant analysis (QDA), K-nearest neighbors (KNNs), AdaBoost (AB), Gaussian Naïve Bayes (GNA), logistic regression (LR), extra trees (ET), random forest (RF), histogram gradient boosting (HGB). Further, deep convolutional neural networks (CNNs) and transfer learning-based Inception ResNet V2 algorithms were used to analyze in the context of deep learning implementations. In this work, the classification of diabetic foot disease through plantar thermograms was conducted using deep learning implementations. The data augmentation is also done to address the paucity of data. Here, deep learning-based models with augmented dataset prove better outcomes.
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Khullar, V., Tiwari, R.G., Agarwal, A.K., Angurala, M. (2023). Transferring Pre-trained Deep CNNs on Plantar Thermograms for Diabetic Foot Disease. In: Marriwala, N., Tripathi, C., Jain, S., Kumar, D. (eds) Mobile Radio Communications and 5G Networks. Lecture Notes in Networks and Systems, vol 588. Springer, Singapore. https://doi.org/10.1007/978-981-19-7982-8_9
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