Muscular Dystrophy (MD) is a group of inherited muscular diseases that are commonly diagnosed with the help of techniques such as muscle biopsy, clinical presentation, and Muscle Magnetic Resonance Imaging (MRI). Among these techniques, Muscle MRI recommends the diagnosis of muscular dystrophy through identification of the patterns that exist in muscle fatty replacement. But the patterns overlap among various diseases whereas there is a lack of knowledge prevalent with regards to disease-specific patterns. Therefore, artificial intelligence techniques can be used in the diagnosis of muscular dystrophies, which enables us to analyze, learn, and predict for the future. In this scenario, the current research article presents an automated muscular dystrophy detection and classification model using Synergic Deep Learning (SDL) method with extreme Gradient Boosting (XGBoost), called SDL-XGBoost. SDL-XGBoost model has been proposed to act as an automated deep learning (DL) model that examines the muscle MRI data and diagnose muscular dystrophies. SDL-XGBoost model employs Kapur's entropy based Region of Interest (RoI) for detection purposes. Besides, SDL-based feature extraction process is applied to derive a useful set of feature vectors. Finally, XGBoost model is employed as a classification approach to determine proper class labels for muscle MRI data. The researcher conducted extensive set of simulations to showcase the superior performance of SDL-XGBoost model. The obtained experimental values highlighted the supremacy of SDL-XGBoost model over other methods in terms of high accuracy being 96.18% and 94.25% classification performance upon DMD and BMD respectively. Therefore, SDL-XGBoost model can help physicians in the diagnosis of muscular dystrophies by identifying the patterns of muscle fatty replacement in muscle MRI.
In 1954, Walton and Nattrass defined Muscular Dystrophy (MD) as a heterogeneous set of primary genetic diseases that impact muscles and is medically characterized by advanced muscular weakness and waste. Psychologically, this group of diseases is united by the occurrence of necrotic and regenerative processes that are frequently related to an increasing number of connective and adipose tissues [
Dystrophinopathies, an X-linked advanced genetic degenerative disease, occurs as a result of deficiency or absence of dystrophin, a sarcolemmal protein. This disease primarily affects the skeletal muscles. Dystrophin and the proteins related to the family form a complex yet essential architecture that works with intracellular actin cytoskeleton to extra-cellular matrix. This association strengthens the sarcolemma from mechanical stress during muscle contraction. It is coded by a very huge chromosome that contains over 2.5 million base pairs and 79 exons. Dystrophin may experience an out-of-the-frame mutation and result in Duchenne Muscular Dystrophy (DMD) while an in-frame mutation may result in milder allelic structure, named as Becker Muscular Dystrophy (BMD). The large scale removal in both DMD and BMD is the most commonly studied mutation type.
DMD, the commonly recorded MD, occurs in a frequency of 1 out of 5000 male children across the globe. The symptoms are typically identified within two years of childbirth by identifying an obvious delay in motor growth. Muscle participation is frequently symmetrical and bilateral. Though similar dystrophin chromosomes undergo mutation, the medical characteristics of BMD differ significantly from DMD in terms of phenotype to no-weak subjective. The onset of disease is experienced at later stages. Calf hypertrophy and muscle cramp are often cited as primary diagnostic features for dystrophinopathies. In such case, multiplex Polymerase Chain Reaction (PCR) should be used for diagnosis [
With advantaged presentation of Medical Magnetic Resonance Imaging (MMRI) in radiographic sciences, the researchers have noticed the capability of MRI to generate high-resolution anatomical images of skeletal muscle. In comparison with previous imaging modes, MRI shows a great difference in between several soft tissue forms. Thus the physicians can investigate separate muscles in sharp difference to adapt fat. In the recent years, muscle MRI has attained extensive medical usage in inflammatory myopathies. This has become possible after the introduction of novel immunosuppressive agent that can precisely diagnose and monitor the responses of human body to the treatment. The capability of MRI to differentiate acute inflammation from chronic fatty replacement in muscle offers a significant predictive data [
Muscle MRI recognizes the feature patterns usually observed in muscle participation and are associated with particular disorders [
The current research article presents an automated muscular dystrophy detection and classification model using Synergic Deep Learning (SDL) with extreme Gradient Boosting (XGBoost), called SDL-XGBoost. The aim of the proposed SDL-XGBoost model is to act as an automated Deep Learning (DL) model that examines muscle MRI data and diagnose the muscular dystrophies. In addition, the proposed SDL-XGBoost model employs Kapur's entropy-based Region of Interest (RoI) detection. Besides, SDL-based feature extraction process is applied to derive a useful set of feature vectors. Finally, XGBoost model is employed as a classification approach to determine proper class labels for muscle MRI data. The novelty of the current study lies in the design of SDL-XGBoost model proposed for diagnosing muscular dystrophies. A comprehensive experimental analysis was performed to showcase the superior performance of SDL-XGBoost model. The results were examined under different aspects. The contributions of the paper are summarized herewith.
An automated muscular dystrophy detection and classification model i.e., SDL-XGBoost model is proposed. Kapur's entropy-based RoI detection and SDL based feature extraction processes are designed. XGBoost model is employed as a classification approach to determine proper class labels for muscle MRI data
Remaining sections of the article are organized as follows. Section 2 offers an overview of deep learning and the existing works. Section 3 introduces the proposed SDL-XGBoost model and the experimental results are discussed in Section 4. At last, Section 5 concludes the work.
The current section briefs the basic concepts in DL models, existing works related to dystrophinopathy diagnosis and summarizes the existing works conducted so far in this domain.
In general, ML techniques undergo training to perform helpful tasks based on manual stimulation. This training process occurs through feature extraction from raw information or through feature learning by additional simply ML techniques. In DL techniques, the system learns the beneficial representation and automate the features from raw information, bypassing manual and problematic phases. DL techniques are gradually being applied in the improvement of medical practices while the healthcare industry is inclining towards technology on a gradual manner. In clinical imaging process, convolutional neural networks (CNN) generates the attention towards DL [
CNN is utilized to enhance the efficiency in radiology practices via protocol determination based on short text classifiers [
Díaz et al. [
MD can be particularly diagnosed based on specific signs and symptoms observed from medical records, physical investigation, and/or muscle biopsy, while the latter is further processed by geneticists through Sanger sequencing method. Recently, numerous muscle participation patterns are labeled for recognition and assistance for diagnostic procedures. But, it has been established that MRI-dependent diagnostic measures, projected for disease diagnosis, are not helpful always in regular healthcare setting, where MRI is examined by doctors. So, the researcher determines that a DL technique will be useful in recognizing the feature patterns that can guides the physicians towards genetic testing. This technique is created to differentiate the disorders with high accuracy over human specialists in the domain. These methods assist better in MD diagnostic procedure. Further, it provides a possible way for further genetic testing or strengthening the existing pathology to find the attained mutation. Researchers consider this as a proof of idea in which AI is employed in the domain of muscle MRI.
The presented SDL-XGBoost model utilizes the muscle MRI patterns to identify the presence of Muscular dystrophy as illustrated in
Kapur [
Let entropy
In
Now, the probability occurrence
The presented SDL technique, represented by
In comparison with conventional DCNN, the presented
The popular remaining network [
In addition, the trained modules of every DCNN are supervised by synergic label of all the pairs of images. The researcher implemented a synergic network that contains embedded layers, whole connected learning and output layers. Here, a pair of images
Next, both the images are concatenated as
To prevent the imbalanced data problem, the proportion of interclass image pairs in all the batches are maintained in the range of 45%–55%. It is comfortable to observe the synergic signal via additional sigmoid layers and utilize the subsequent binary cross-entropy loss which are defined herewith.
The presented
and
When employing the trained
XGBoost is a supervised EL technique that simulates a generalization gradient boosting technique. A regulation term is involved in this technique to produce accuracy through multicore and distributed settings for classifiers, regression and ranking task [
When dependent loss function in Taylor expansion i.e.,
where
A DT forecasts a constant value over a leaf. Next, a tree
In suitable structure tree, the objective function is minimalized to
When substituting the formula by
This equation is utilized in the practice to assess the splitting applicants in XGBoost. In order to find the optimum split, precise greedy technique and global and local (repropose applicants next every splits) variant approximation techniques are run for every possible splitting of the entire feature. This process is carried out by processing every splitting applicant in early stage, and similar split procedure is used to find the split on every leaf [
In XGBoost by DART booster,
Let
Let
The current section validates the results of analysis of the proposed SDL-XGBoost model on the classification of DMD and BMD using muscle MRI images. The presented SDL-XGBoost model was simulated in Python 3.6.5 tool. A detailed comparative study was also conducted with recent state-of- the-art methods in terms of accuracy, sensitivity, and specificity.
No. of Runs | Accuracy | Precision | Sensitivity | Specificity | F-Score | Kappa |
---|---|---|---|---|---|---|
Run - 1 | 93.45 | 80.52 | 95.38 | 92.86 | 87.32 | 0.1706 |
Run - 2 | 94.36 | 82.78 | 96.15 | 93.81 | 88.97 | 0.1736 |
Run - 3 | 95.27 | 84.67 | 97.69 | 94.52 | 90.71 | 0.1779 |
Run - 4 | 95.64 | 85.81 | 97.69 | 95.00 | 91.37 | 0.1787 |
Run - 5 | 96.00 | 86.49 | 98.46 | 95.24 | 92.09 | 0.1806 |
Run - 6 | 96.36 | 87.67 | 98.46 | 95.71 | 92.75 | 0.1814 |
Run - 7 | 96.91 | 89.51 | 98.46 | 96.43 | 93.77 | 0.1825 |
Run - 8 | 97.27 | 90.78 | 98.46 | 96.90 | 94.46 | 0.1832 |
Run - 9 | 97.64 | 91.49 | 99.23 | 97.14 | 95.20 | 0.1852 |
Run - 10 | 98.91 | 96.27 | 99.23 | 98.81 | 97.73 | 0.1878 |
Average | 96.18 | 87.60 | 97.92 | 95.64 | 92.44 | 0.1800 |
No. of Runs | Accuracy | Precision | Sensitivity | Specificity | F-Score | Kappa |
---|---|---|---|---|---|---|
Run - 1 | 90.73 | 71.55 | 84.69 | 92.14 | 77.57 | 0.1231 |
Run - 2 | 91.89 | 74.14 | 87.76 | 92.86 | 80.37 | 0.1291 |
Run - 3 | 92.47 | 75.65 | 88.78 | 93.33 | 81.69 | 0.1315 |
Run - 4 | 93.24 | 77.88 | 89.80 | 94.05 | 83.41 | 0.1341 |
Run - 5 | 93.63 | 79.28 | 89.80 | 94.52 | 84.21 | 0.1348 |
Run - 6 | 94.59 | 81.82 | 91.84 | 95.24 | 86.54 | 0.1392 |
Run - 7 | 94.98 | 82.73 | 92.86 | 95.48 | 87.50 | 0.1412 |
Run - 8 | 96.14 | 86.11 | 94.90 | 96.43 | 90.29 | 0.1458 |
Run - 9 | 96.91 | 88.68 | 95.92 | 97.14 | 92.16 | 0.1485 |
Run - 10 | 97.88 | 91.43 | 97.96 | 97.86 | 94.58 | 0.1528 |
Average | 94.25 | 80.93 | 91.43 | 94.91 | 85.83 | 0.1400 |
Finally, the proposed SDL-XGBoost technique classified BMD
To further validate the supremacy of the presented SDL-XGBoost model, a detailed comparative analysis was conducted and the results were compared in terms of three distinct measures as given in
Models | Specificity | Sensitivity | Accuracy |
---|---|---|---|
SDL-XGBoost (DMD |
0.98 | 0.96 | 0.96 |
SDL-XGBoost (BMD |
0.91 | 0.95 | 0.94 |
VGG-19 | 0.98 | 0.66 | 0.87 |
ResNet-50 | 0.92 | 0.89 | 0.91 |
DenseNet-201 | 0.98 | 0.74 | 0.90 |
DenseNet-121 | 0.94 | 0.78 | 0.88 |
lnception-V3 | 0.94 | 0.83 | 0.90 |
The experimental values depict that VGG-19 model produced insignificant outcome with a sensitivity of 0.98, specificity of 0.66, and accuracy of 0.87. At the same time, the DenseNet-121 model accomplished a slightly enhanced classification result with a sensitivity of 0.94, specificity of 0.78, and accuracy of 0.88. Followed by, the Inception v3 model attained a certain increase in the outcome in terms of sensitivity 0.94, specificity 0.83, and accuracy 0.90. Moreover, the DesnseNet-201 model offered a moderate performance with a sensitivity of 0.98, specificity of 0.74, and accuracy of 0.90. Furthermore, the ResNet-50 model showcased moderate outcomes with a sensitivity of 0.92, specificity of 0.89, and accuracy of 0.91. But the presented SDL-XGBoost model produced enhanced outcomes by classifying BMD with a sensitivity of 0.91, specificity of 0.95, and accuracy of 0.94. At last, the SDL-XGBoost model has classified the DMD with a sensitivity of 0.98, specificity of 0.96, and accuracy of 0.96.
The current research article developed an automated muscular dystrophy detection and classification model using SDL-XGBoost. The presented SDL-XGBoost model makes use of muscle MRI patterns to identify the presence of muscular dystrophy. The proposed SDL-XGBoost model involves three major processes such as RoI detection, feature selection, and classification. Primarily, Kapur's thresholding is applied to determine the regions of interest. Afterwards, SDL model is employed to derive a set of useful feature vectors. Finally, XGBoost model is utilized in the allocation of appropriate class labels for muscle MRI data. A comprehensive experimental analysis was conducted to showcase the superior performance of the proposed SDL-XGBoost model. The results were examined under different aspects which inferred the supremacy of the proposed model. So, SDL-XGBoost model has been proved experimentally and can assist physicians in diagnosing muscular dystrophies through muscle fatty replacement patterns in muscle MRI. In future, the detection rate of muscular dystrophies can be improved with the help of learning rate schedulers.
The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work under grant number (RGP1/147/42), Received by Fahd N. Al-Wesabi.