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Bakshi making [10]. Our design would rely on a slightly modified CNN architecture. Aim of This Study This designed system can help a physician draw a correct boundary between what type of knees should require surgery and which type of knee should not require surgery but should still be classified as knock knees for medical assess- ment purposes.

Table of contents

However, a different approach has been taken for this we are trying to relate how the model can predict if a patient has Genu Valgum from the X-ray-mapping as a test set. This system can also be extended to the use as a screening tool by sports in- structors to identify the severity of knock knees in them which could impair running and cause knee pain if left un-treated for extended period of time 2. Methods The focus here is in investigating which images that are fed into the system are correctly classified under the label of knock knees in order to evaluate the per- formance of our network.

As stated in [12] that MLP yielded higher accuracy for categorization of medical images we use the same algorithm but in a different classification task. The dataset is a curated list of images of patients suffering from Genu Valgum which have been generated from various sources of which few of them have been obtained from Physiobank which a very good repository for valid medical data.

However, the major goal is the correct decision-making process on the unknown image which would be feed from a live volunteered subject. A tuned CNN has been used. The process is explained in the next section.

Use of Adam: Adam is a very versatile algorithm for gradient descent when it comes to updating of weights. Research has shown that Adam is used as a bench marker for most neural networks. Paper [13] explains that Adam is pretty straightforward to implement and is computationally efficient and have low memory requirements.

It also is immune to diagonal rescaling of gradients and is well suited for large amounts of data [13]. AdaGrad is adaptive gradient i.


Adam considers both these features Before the CNN can be initialized there are a few considerations mainly relat- ing to the number of images in the training and test set, what would be the learning parameter, what should be the batch size and what would be the ideal epoch for this case.

The GridSearch technique has been used from keras library in this case for the selection of the best hyperparameters in this model. There is still a problem with the existing dataset firstly the images which have been chosen in the dataset are not of the same size and also the size of the data- base is very limited 20 to 40 images in either of the training and test sets.

This is very beneficial since the number of images can be augmented from the parent dataset by applying these transformations.

This has been performed in the image pre-processing step. Figure 1 gives the entire process of the modified network. More literature on the working of CNN networks can be referred form this ar- ticle here [14].

The steps involved in the network have been described in the representation above. Figure 1.

Computer Networks

Modified CNN architecture. DOI: Bakshi 3.

Results and Evaluation 3. Testing on Normal to Normal Mapping Figure 2 shows the sample test image set which have been used. The parameters are chosen based on the visibility of the exposed portions of the knee.

Social Networks and Surveillance for Society

Some parts of the images have been cropped so as to yield more information. However, this is completely at the discretion of the operator considering the application to focus in. The best parameters were chosen by making use of grid search method as explained in the diagram in Figure 1 However this accuracy can be further improved by making use of more epochs and supplying more varied batch sizes. Figure 3 shows the unknown image which was used on the network to check the successful prediction.

However, the subject was also made to closely simulate a Genu Valgum to which the resulting predication was very successful Figure 4. Note: This study does not involve any kind of risk not greater than the daily dangers a person might face, a signed consent deemed to be not necessary by the Ethics Board.

Figure 5 shows a simulated Genu Valgum which was done to test the validity and performance of the model.

Figure 6 shows the results for this image. Testing on X-Ray to Normal Mapping For the training set we now introduce X-ray images to compare the performance of the network and to evaluate if neural networks have the ability to co-relate between the two or not.

Figure 2. Sample Test set All copyrights of individual images acknowledged.

Figure 3. Unknown image of control sample. Bakshi Figure 4. Subject yielded normal result. Figure 5. Simulated Genu Valgum from the dataset.

Figure 6. Power Sources for Wireless Sensor Networks. Pages Networked Slepian-Wolf: Theory and Algorithms. Context-Aware Sensors.

Providing Seamless Communication in Mobile Wireless Networks

Silence Is Golden with High Probability: Topology Transparent Support for Sensor Networks. Sturm, Werner Weber. Lessons from a Sensor Network Expedition.

Back Matter. Wireless sensor networks are a key technology for new ways of interaction betweencomputersandthephysicalenvironmentwhichsurroundsus. Compared to traditional networking technologies, wireless sensor networks are faced with a rather unique mix of challenges: The goal of this workshop is to create a forum for presenting new results in the?

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