AN AUTOMATIC DERMATOLOGY DIAGNOSIS USING CONVOLUTIONAL NEURAL NETWORK

1. AN AUTOMATIC DERMATOLOGY
DIAGNOSIS USING CONVOLUTIONAL
NEURAL NETWORK
PRESENTED BY:
ADJEI-MENSAH, ISAAC - 201824090007
KUUPOLE ERU-BAAR, EWALD - 201824010002
BAIDOO, CHARLESETTA - 201824090004
OWUSU ANSAH, ERNEST - 201824110004
UNIVERSITY OF ELECTRONIC SCIENCE AND TECHNOLOGY OF
CHINA
COURSE INSTRUCTOR: PROFESSOR YANFEI JING
DATE: DECEMBER 24, 2018.

2. SOME KEYWORDS
ABBREVIATIONS
CNN - CONVULUTIONAL NEURAL NETWORK
CONV - CONVOLUTION
POOL - POOLING
FC - FULLY CONNECTED
DFU - DIABETIC FOOT ULCERS
LPLK - LICHEN-PLANUS LIKE KERATOSES
NV - MELANOCYTIC NEVI
MEL - MELANOMA
BKL - BENIGN KERATOSIS
BCC - BASAL CELL CARCINOMA
AKIEC - ACTINIC KERATOSES
VASC - VASCULAR SKIN LESIONS
DF - DERMATOFIBROMA
HAM10000 - HUMAN AGAINST MACHINE WITH 10000 TRAINING IMAGES

3. INTRODUCTION
DEFINITION OF DERMATOLOGY
Dermatology is a term in the field of health used to refer to the skin, hair, and nails.
CONVOLUTIONAL NEURAL NETWORK (CNN)
A convolutional neural network is a type network, artificially that uses perceptrons, a
machine learning algorithm, by supervising learning, in order to analyse data.
CNN’s can be applied to the fields of image processing, natural language processing,
and other kinds of cognitive tasks.
CONVOLUTION
This is just a singular aspect of the building blocks of CONVOLUTIONAL NETWORK. The
basic reason for a convolution is to extract features from the image inputted in

4. EXAMPLE OF AN IMAGE CLASSIFICATION

5. HISTORY OF CNN FOR SKIN DISEASES
 Rathod et. al used convolutional neural network in extracting images features and
classifying them based on an algorithm called the SOFTMAX classifier and had a report
based on the diagnosis as a skin disease.
 Abu and Hasan used a regular CNN to detect melanoma images
 M. Goyal et. al used CNN in extracting the features of Diabetic Foot Ulcers (DFU) and
that of healthy skin patches, in order to understand the differences from the perspective
of computer vision.

6. TYPES OF CONVOLUTIONAL NEURAL NETWORKS
All convolutional neural networks are built based on
layers. Below are the three layers of a convolutional
neural network;
 Convolutional Layer
 Pooling Layer, and
 Fully-Connected Layer

7. CLASSIFICATION OF AN OBJECT IN LAYERS

8. PROBLEM STATEMENT
The question we brought on board as a task to solve was,
CAN WE USE MATRICES OR FILTERS TO EXTRACT FEATURES FROM IMAGES IN
TO ACCURATELY CLASSIFY DERMATOLOGY DISEASES?
In this work, we sought to construct a CNN model to automate the diagnosing of
dermatology diseases.

9. DATASET TO BE USED IN THE EXPERIMENT
The HAM10000 Dataset is what was collected to be used for
this experiment. A collection of dermatoscopic images from
different human races were acquired and then stored for
different modalities as the diseases classified under
dermatology is of seven (7) different kinds. In this, the final
dataset consists of 13, 786 dermatoscopic images of which
10010 were released as a training set for academic machine
learning purposes and is made publicly available.

10. CLASSES OF THE DATASET
 ACTINIC KERATOSES (AKIEC): This skin disease is a type of skin disease
that can be treated without the need for a surgery.
BASAL CELL CARCINOMA (BCC): An epithelial skin cancer that rarely
metastasizes but grows destructively if not treated.
 BENIGN KERATOSIS (BKL): A generic class that includes seborrheic
keratosis, solar lentigo whuch could be regarded as a flat variant of
seborrheic keratosis.
 DERMATOFIBROMA (DF): A benign skin lesion regarded as either a
benign proliferation or an inflammatory reaction to minimal trauma.

11. CLASSES OF THE DATASET CONT’D
 MELANOCYTIC NEVI (NV): These are benign neoplasms of
melanocytes and appears in a myriad of variants.
 MELANOMA (MEL): This type of skin disease is derived from
melanocytes and appears in different variants.
 VASCULAR (VASC): These are the datasets range from
cherry angiomas to angiokeratomas and pyogenic
granulomas. In this type of skin lesions, haemorrhage is found
amongst it.

12. OBJECTIVE OF THE PROJECT
 Automatic classification of images into one of the
disease under consideration in the dataset.
 Visualization of filters.

13. PROJECT SIGNIFICANCE
 Understanding how matrices are used to solve clinical issues
 How the classification and filtering will be done.

14. PROPOSED MODEL
 This paper seeks to propose an automated diagnosis of pigmented skin
lesions. Our proposed model is a structured convolutional neural
network that has fully connected layers.
 Followed by a max-pooling, to downsize the image input flowing
through the network.
 ReLU was used as an activation function so as to avoid the vanishing
gradient problem.
 Data augmentation techniques was used to increase training samples
by 50% in order to avoid overfitting.
 After the last convolutional layer, a flattening is done and a 1D tensor
fed into the fully connected layers.
 The last layer applies softmax activation for classification to be done.

15. PROPOSED MODEL ARCHITECTURE

16. AUGMENTED DATASET
The following augmented techniques were performed in order
to balance data samples in each class of the dataset;
 rescaling images to 1./255 to transform each pixel from [0,
255] to [0, 1]
 image shearing clockwise value of 0, 2
 a random zoom range of 40
 image width and height shifting of 0.2
 random horizontal flipping

17. AUGMENTED DATASET CONT’D
CLASS ORIGINAL SAMPLES NUMBER AUGMENTED SAMPLES
NUMBER
nv 6705 6705
mel 1113 5959
bkl 1099 5995
bcc 514 5888
akiec 327 5243
vasc 142 5301
df 115 4416
Total 10015 39507

18. IMPLEMENTATION
 Six convolutional layers were used with an input image size of 224 x 224 x 3
 After 32 filters had been passed on the layers, an output of 222 x 222 x 32 was
derived.
 Max-pooling was then applied after every convolution, hence an new output of 111 x
111 x 32, after a 2 x 2 max-pooling was performed.
 New output became, 109 x 109 x 64, with a max-pooling of 54 x 54 x 64
 Third convolution, 54 x 54 x 64, output 52 x 52 x 90 with a max-pooling of 26 x 26 x
90
 Final max-pooling output 1 x 1 x 128, in this flattening was done to obtain a column
where fully connected can be performed, and in this the number of the seven
dermatological diseases the architecture was made to train was derived at.
 Batch size of 20 was maintained, training was done with the Adan Optimizer at a
learning rate of 0.001, and training was done for 20 epochs.

19. RESULTS (LOSS)

20. RESULTS (LOSS) CONT’D
From the figure above, it is well noting that
a loss of 1.65% was made from the
onslaught of the HAM10000 datasets, and
this loss remained a continual fall, and fell to
0.23%. As this can be seen, the dataset
learnt what sets was fed into it.

21. RESULTS (ACCURACY)

22. RESULTS (ACCURACY) CONT’D
From the above figure, it can be seen
that there was a steady rise as the
datasets were first trained, as the rise
first begun from 22% and gradually
ended at a percentage of 82% after
several datasets were performed.