Study on Cloud Image Processing and Analysis Based Flatfoot Classification Approach

Jian, Ming-Shen; Shen, Jui-Lung; Chen, Yu‐Chih; Chang, Chao‐Chun; Fang, Yi-Chi; Chen, Ci-Cheng; Chen, Wei-Han

doi:10.9734/bpi/ctmcs/v5/3396f

Cited by 2 publications

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“…Mainly, using the traditional image analysis technique involves defining bone edges and then implementing different mathematical equations to search along the bone edges for the PoI location. The system proposed by Jian et al [23] measures foot Arch Angle to detect flat feet from lateral X-ray images. The method started with outlining the foot edges of each image using Canny Edge detection, and the noise was reduced by Gaussian blur filters.…”

Section: Related Workmentioning

confidence: 99%

Flatfeet Severity-Level Detection Based on Alignment Measuring

Alsaidi,

Moria

2023

Sensors

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Flat foot is a postural deformity in which the plantar part of the foot is either completely or partially contacted with the ground. In recent clinical practices, X-ray radiographs have been introduced to detect flat feet because they are more affordable to many clinics than using specialized devices. This research aims to develop an automated model that detects flat foot cases and their severity levels from lateral foot X-ray images by measuring three different foot angles: the Arch Angle, Meary’s Angle, and the Calcaneal Inclination Angle. Since these angles are formed by connecting a set of points on the image, Template Matching is used to allocate a set of potential points for each angle, and then a classifier is used to select the points with the highest predicted likelihood to be the correct point. Inspired by literature, this research constructed and compared two models: a Convolutional Neural Network-based model and a Random Forest-based model. These models were trained on 8000 images and tested on 240 unseen cases. As a result, the highest overall accuracy rate was 93.13% achieved by the Random Forest model, with mean values for all foot types (normal foot, mild flat foot, and moderate flat foot) being: 93.38 precision, 92.56 recall, 96.46 specificity, 95.42 accuracy, and 92.90 F-Score. The main conclusions that were deduced from this research are: (1) Using transfer learning (VGG-16) as a feature-extractor-only, in addition to image augmentation, has greatly increased the overall accuracy rate. (2) Relying on three different foot angles shows more accurate estimations than measuring a single foot angle.

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Section: Related Workmentioning

confidence: 99%