=Paper=
{{Paper
|id=Vol-2823/Paper9
|storemode=property
|title=AMD-Network: Automatic Macular Diagnoses of disease in OCT scan images through Neural Network
|pdfUrl=https://ceur-ws.org/Vol-2823/Paper9.pdf
|volume=Vol-2823
|authors=Praveen Mittal, Charul Bhatnagar
}}
==AMD-Network: Automatic Macular Diagnoses of disease in OCT scan images through Neural Network==
https://ceur-ws.org/Vol-2823/Paper9.pdf
AMD-Network: Automatic Macular Diagnoses of disease
in OCT scan images through Neural Network
Praveen Mittal, Charul Bhatnagar
G.L.A University, Mathura, Uttar Pradesh, India
Abstract Retinal optical coherence tomography scan images are used to diagnose retinal diseases using
Convolutional Neural Network. One of the most beneficial of using Optical coherence tomography scan
images is its non-invasive operation. Most ophthalmologists are using optical coherence tomography images
for treating the retinal disorder in the human eye but due to its high cost of imaging, every patient can effort
this imaging modality. Convolution Neural networks are now day giving lots of opportunities to classify
various classes of images automatically. This method first removes noise from the images which get induced
at the time of image capturing. Usually, Gaussian noise easily gets introduces in the transmission of images
from one device to another. Speckle noise can be easily removed with the help of an average filter with a
deviation of 0.7. Convolution Neural Network first trains the network with the help of activation functions
like rectilinear Unit. The proposed method achieves 98.8% accuracy in the dataset of 50000 images
Keywords Choroidal Neovascularization, Diabetic retinopathy, Diabetic Macular Edema,
Glaucoma, Human Retinal Disease, Residual
Network, VGG16.
deviation of 0.7 and mask (9,9) could efficiently
1 Introduction remove noise from the scanned images [6].
The human eye plays a vital role in training the Ophthalmology is evolving by many research
human brain. Retinal diseases such as diabetic scholars [7]. Eyes being a major part of the human
macular edema, glaucoma, diabetic retinopathy, body require advanced techniques for detection and
and Choroidal Neovascularization not only impact treatment of various diseases - OCT is one such
the vision of the human eye but also made a great technique for imaging [8].
impact on learning the visual object's behavior [1].
Optical Coherence Tomography has become a Handling OCT images becomes a tedious task due
useful imaging modality now a day due to its non- to various issues such as noise in the image, less
invasive medical imaging method [2]. Retinal visibility, unnoticeable variation in intensity
Optical Coherence Tomography allows between different layers of Retina, etc. OCT
ophthalmologists to diagnose retinal diseases such images are used to examine various eye diseases
as age-related macular degeneration and glaucoma and to know the condition of the retinal layout [9].
[3]. Therefore, it stings a very important role in the
field of Ophthalmology [10].
Processing Optical coherence topographical images
is not an easy task due to speckle noise present in Denoising is usually one of the major steps in
the images at the time of scanning [4]. classification pre-processing [11]. There is many
convolutional neural networks (CNN) based
Many research scholars [5] gave techniques to algorithm as mentioned in [12] on a chest X-ray
remove these speckle noise from optical coherence dataset to classify medical Images of pneumonia.
tomography but Gaussians filter with a standard We have hooked upon OCT images for this process
as they have a huge relevance when it comes to
generating a schematic that defines multiple tissues
ACI’2021: Workshop on Advances in Computational of the eye, called the Retina [13]. It is also used for
Intelligence, February 25-27, 2021, Delhi, India
EMAIL: praveen.mittal@gla.ac.in (P. Mittal); charul@gla.ac.in examination, by imaging the eyes of the patients
(C. Bhatnagar) with various eye conditions such as Diabetic
ORCID: 0000-0002-5331-7154 (P Mittal); Macular edema and diabetic retinopathy, etc [14].
Noise makes its way into the OCT images during
© 2020 Copyright for this paper by its authors. Use permitted under
Creative Commons License Attribution 4.0 International (CC BY 4.0). acquisition. The sensor and circuitry of
CEUR Workshop Proceedings (CEUR-WS.org) a scanner or digital camera could also raise the
problem of getting noise to the image [15]. Further
film grain can also add to be a reason behind image images and work further on the results obtained
noise. Never intended to be introduced in an image [19]. Here, Technique aim at diagnosing a various
it degrades the quality of an image. Hence, disease that comprises the retina of the eye. The
processing an image becomes necessary [16]. method needs to find the thickness of each layer
Image processing is the procedure of improving the after finding different layers, to examine the eye for
quality and information content of the original data. various diseases. Different layers may consist of
Image enhancement, Restoration is amongst some different diseases which need to be diagnosed.
relief approaches that are used to improve the What adds to the problem is that the gradient
quality of an image. The latest research in [17] amongst the layers is decent and hence it further
expresses the application of deep learning in becomes a tedious task to segregate the layers. In
medical image processing. this paper, AMD-net is used that works on cross-
validation which aims to figure the classification
2 Related Work hence, generating the desired classification or
There are the number of classification work has clustering of images.
been done for OCT images till date, but they are for
classifying diseased eye from Normal eye. The
following section describes the various works done
in the classification of retinal Spectral Domain
OCT (SDOCT) images.
M. Treder et al. proposed a model in [5], [18],
which is based on machine learning for retinal
SDOCT image categorization for Age-related
Macular Degeneration with the help of a dataset
obtained from Heidelberg. The authors use the
Inception-v3 model for Deep Convolutional Neural
Network where starting layer got trained on
ImageNet and the final layer got trained for taken
dataset. Their work showed a good result for age-
related macular degeneration diseases. Their work
was only designed for age-related macular
degeneration disease and Normal eye images Fig. 2. Accumulation of retinal fluid in
layers [17]
3 Dataset
Dataset of optical coherence tomographic retinal
images which is used in this proposed work is
freely available on the Kaggle repository. In the
taken dataset there are four classes of images
names Diabetic Macular edema, Diabetic
Retinopathy, Glaucoma, and Healthy eye. Total
50000 images over which convolutional neural
networks have been trained.
4 Proposed Work
The proposed word showed the classification of
retinal images with an accuracy of 98.8%. This
work uses Neural Network to train the neurons in
the network and provide a feature vector for the
Fig. 1. OCT scanning of the human eye [18]. identification of features of the particular disease.
The next step that comes is Segmentation.
Segmentation is a process of dividing an image into
regions. This technique is a mid-level processing
technique. This further aims to segment the OCT
Convolutn_two_dim(submatrics=16, Convolutn @5 (Convtn2Dm) (Null, 59, 59, 32) 4640
mask_shape=(5,5), S_rate=1, stuffing='valid', ___________________________________________________
act_funtn ='relunit', input_shape=size) Convolutn @6 (Convtn2Dm) (Null, 57, 57, 32) 9248
___________________________________________________
Convolutn_two_dim (submatrics =16, mask_shape Maxpoollayer@3 (MaxPoolingtwodim (Null, 28, 28, 32) 0
___________________________________________________
=(5,5), S_rate =1, stuffing ='valid', act_funtn
Convolutn@7 (Convtn2Dm) (Null, 26, 26, 64) 18496
=''relunit ') ___________________________________________________
Convolutn@8 (Convtn2Dm) (Null, 24, 24, 64) 36928
MxPoolingLayer_two_dim(pool_sz=(4,4)) ___________________________________________________
Maxpoollayer@4 (MaxPooling2dm (Null, 12, 12, 64) 0
Convolutn_two_dim (submatrics =32, mask_shape ___________________________________________________
=(5,5), S_rate =1, stuffing ='valid', act_funtn loosingvalues@1 (Dropout) (Null, 12, 12, 64) 0
___________________________________________________
=''relunit ') flattenofcurvature@1 (Flatten) (Null, 9216) 0
___________________________________________________
Convolutn_two_dim submatrics =32, mask_shape compact_1 (Compact) (Null, 128) 1179776
=(5,5), S_rate =1, stuffing ='valid', act_funtn ___________________________________________________
compact_2 (Compact) (Null, 4) 516
='relunit ')
==============================================
Overall attribute: 2,153,756
MxPoolingLayer_two_dim (pool_sz=(4,4)) learnable attribute: 2,153,756
Non-learnable attribute: 0
Convolutn_two_dim (submatrics =64, mask_shape
=(5,5), S_rate =1, stuffing ='valid', act_funtn
=''relunit ') 5 Results and Analysis
Convolutn_two_dim (submatrics =64, Implementation results are elaborated in Table 2
mask_shape =(5,5), S_rate =1, stuffing ='valid', for the hyper attribute of the experimented network.
act_funtn ='relunit ') Table 2 Hyper attribute of the experimented
network
MxPoolingLayer_two_dim (pool_sz=(4,4))
Hyper Value
Convolutn_two_dim (submatrics =128, attribute
Damage ‘categorical
mask_shape =(5,5), S_rate =1, stuffing ='valid', Tiny group size cross-entropy
200
act_funtn =''relunit ')
Epoch 250
Convolutn_two_dim (submatrics =128, Premature 12 val_loss
mask_shape =(5,5), S_rate =1, stuffing ='valid', Typical Barrier Was_opt_5
act_funtn='relunit') stuffing ‘valid’
Optimization ‘diffGrad’
MxPoolingLayer_two_dim (pool_sz=(4,4)) algorithm
multiprocessing ‘Null’
Table 1. Classification of OCT Scan images as per
the classes.
___________________________________________________
Layer (type) Output Shape Param #
==============================================
Convolutn @1 (Convtn2Dm) (Null, 254, 254, 8) 80
___________________________________________________
Convolutn @2 (Convtn2Dm) (Null, 252, 252, 8) 584
___________________________________________________
Maxpoollayer@1 (MaxPoolingtwodim (Null, 126, 126, 8) 0
___________________________________________________
Convolutn @3 (Convtn2Dm) (Null, 124, 124, 16) 1168
___________________________________________________ Fig. 3 Training and testing values of the Network
Convolutn @4 (Convtn2Dm) (Null, 122, 122, 16) 2320 on retinal images
___________________________________________________
Maxpoollayer@2 (MaxPoolingtwodim (Null, 61, 61, 16) 0
___________________________________________________
Table 3 Confusion matrix for the proposed model Further, the classification time of the experimented
convolutional neural network is 1.061 sec which is
19 sec less than the time taken by ResNet50 for the
OCT Images set of retinal images. So if we talk about the total
23 time taken to process the retinal OCT images for
DR 5 3 2
4 classification is 1.173 sec, which is less than the
True label
DME 3 254 2 4 time taken by ImageNet and ResNet50 on the same
Glau- retinal OCT images.
coma
21 2 237 6
Healt
hy
1 1 1 219 7 References
DR DME Glaucoma Healthy
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