an algorithm for developing a lymph node detection system using DCNN [4]. Based on
the above successful experience of DCNN applied to medical images, we tried to use
DCNN to classify radioactive musculoskeletal. However, the images currently used to
train the DCNN model have the following drawbacks:
1. The quality of the training images is not high.
2. For radioactive image datasets, DCNN does not extract its texture features very
well.
Therefore, this paper proposes to preprocess the image to improve the image
quality, then fuse the shallow texture features and then send them into the Densnet169
network for classification, and compare the classification effects without using the
shallow texture features.
2 Musculoskeletal Abnormality Diagnosis Based on Densenet
The method proposed in this paper is shown in Fig. 1 including image preprocessing,
image fusion, and training depth neural network.
2.1 Preprocessing
Histogram equalization is a method of enhancing image contrast (image contrast). In
experiments, we used contrast limited adaptive histogram equalization (CLAHE) [5]
for image enhancement. The histogram value of the CLAHE method is:
Hist
0
ðiÞ ¼
HistðiÞ þ L HistðiÞ\T
H max
HistðiÞ ! T
Fig. 1. The structure of the algorithm
Abnormality Diagnosis of Musculoskeletal Radiographs Combined …
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