Fusion of Structural and Textural Features
for Melanoma Recognition
PROJECT REPORT
Submitted by
MANTHRA R (312416104062)
MARY MATHALIN A (312416104063)
in partial fulfilment for the requirement of award of the degree
of
BACHELOR OF ENGINEERING
in
COMPUTER SCIENCE ENGINEERING
St. JOSEPH’S INSTITUTE OF TECHNOLOGY
ANNA UNIVERSITY: CHENNAI 600 025
DECEMBER 2019
CHAPTER
TITLE
PAGE
I
1
INTRODUCTION
1.1 System Overview
1.2 Scope of the Project
2
3
2
LITERATURE SURVEY
2.1 A Scalable and Reliable Mobile Code Offloading Solution
2.2 Energy saving offloading scheme for mobile cloud computing using
Cloudsim.
2.3 Overview of Offloading in Smart Mobile Devices for Mobile Cloud
Computing
2.4 Review of Offloading Approaches in Mobile Cloud Computing
3
SYSTEM ANALYSIS
3.1 Existing System
3.1.1 Disadvantages
3.2 Proposed System
3.2.1 Advantages
3.3 Requirements
3.3.1Algorithms
4
SYSTEM ARCHITECTURE
4.1 Architectural Description
5
SYSTEM IMPLEMENTATION
5.1 Deep Residual Neural Network
5.2 Image Preprocessing and Data Augmentation
5.3 Kernel-based Classification
5.4 Modules Used
I
4
4
4
4
5
5
6
6
6
7
8
ABSTRACT
Melanoma skin cancer is one of the most rapidly increasing
and deadliest cancers in the world, which accounts for 75% of skin cancer
deaths [1-3]. Early diagnosis is of great importance for treating this disease
as it can be cured easily at early stages [1-4]. To improve the diagnosis of this
disease, dermoscopy has been introduced to assist dermatologists in clinical
examination since it is a non-invasive skin imaging technique that provides
clinicia
we present a novel framework for dermoscopy image
recognition via both a deep learning method and a local descriptor
encoding strategy. Specifically, the deep representations of a rescaled
dermoscopy image are first extracted via a very deep residual neural
network (ResNet) pre-trained on a large natural image dataset. Then these
local deep descriptors are aggregated by orderless visual statistic features
based on fisher vector (FV) encoding to build a global image
representation. Finally, the FV encoded representations are used to classify
melanoma images using a support vector machine (SVM) with a Chisquared kernel. Our proposed method is capable of generating more
discriminative features to deal with large variations within melanoma
classes as well as small variations between melanoma and non-melanoma
classes with limited training data. Extensive experiments are performed to
demonstrate the effectiveness of our proposed method. Comparisons with
state-of-the-art methods show the superiority of our method using the
publicly available ISBI 2016 Skin lesion challenge dataset.
II
CHAPTER 1
INTRODUCTION
1.1 SYSTEM OVERVIEW
The objective of the paper is to find better and more efficient
ways to automatically detect early malignant melanoma using digital image
processing the techniques. This paper focuses on the preprocessing stage,
the image analysis of the tumor. The detection of Melanoma cancer in early
stage can be helpful to cure it.
1.2 SCOPE OF THE PROJECT
we have focused on developing CAD systems for skin cancer
detection. In hospitals, to detect the melanoma tissues, patients generally
undergo a skin examination using the skin surface microscopy techniques
commonly known as dermoscopy. The structural features are obtained from
multiresolution analyses which are used to discriminate the structures as
borders, dots and streaks.
On the other side, the textural features computed by LBP
operators are used to discriminate the local variation of colours, the pigment
network etc. Later, these features are fused in multiple combinations to
investigate the influence of each combination in the performance of
melanoma detection.
1
CHAPTER 2
LITERATURE SURVEY
2.1 A Scalable and Reliable Mobile Code Offloading Solution:
Code offloading is a popular technique for extending the natural capabilities of
mobile devices by migrating processor-intensive tasks to resource-rich surrogates.
Despite multiple platforms for offloading being available in academia, these
frameworks have yet to permeate the industry. One of the primary reasons for this is
limited experimentation in practical settings and lack of reliability, scalability, and
options for distribution. This paper introduces MobiCOP, a new code offloading
framework designed from the ground up with these requirements in mind. It features a
novel design fully self-contained in a library and offers compatibility with most stock
Android devices available today. Compared to local task executions, MobiCOP offers
perfmance improvements of up to 17x and increased battery efficiency of up to 25x,
shows minimum performance degradation in environments with unstable networks, and
features an autoscaling module that allows its server counterpart to scale to an arbitrary
number of offloading requests. It is compatible with the most relevant Android
technologies optimized for heavy computation (NDK and Renderscript) and has so far
been well received by fellow mobile developers. We hope MobiCOP will help bring
mobile code offloading closer to the industry realm.
2.2 Energy saving offloading scheme for mobile cloud computing
using CloudSim:
we accentuate on offloading for sparing energy in mobile cloud computing
MCC. MCC is emerging as a noticeable research area that is looking to bring the
massive advantages of the cloud to the constrained mobile devices. The impediments
of poor processing capacity and constrained battery life make it troublesome for mobile
devices to process complex calculation undertakings. The energy system concentrates
on mobile devices battery energy that utilises computational offloading for
computationally serious mobile applications on the mobile devices. Each time an
application in the physical mobile devices is initialised, the power utilisation is
ascertained by the structure and it decides whether to make on offloading choice or not.
This paper proposes a novel energy saving computational offloading framework for the
preparing of serious mobile applications in MCC. It is found that energy utilisation of
the selected application reduces up to 80.94% and execution time reduces up to 97.16%
by computational offloading utilising CloudSim when contrasted with the conventional
methods. It enhances the quality of services for mobiles and helps in keeping up
constant reactions for mobile applications.
2
2.3
Overview of Offloading in Smart Mobile Devices for Mobile Cloud
Computing :
Roopali, Rajkumari
The recent advancement in cloud computing is leading to an excessive growth
of mobile devices that can become powerful means for information access and mobile
applications. Thus introducing a latent technology called Mobile Cloud Computing
(MCC). Smartphone devices support wide range of mobile applications which require
high computational power, memory, storage and energy but these resources are limited
in number so act as constraints in smartphone devices. With the integration of cloud
computing and mobile applications it is possible to overcome these constraints by
offloading the complex modules on cloud. This review of literature focuses on
challenges in offloading such as latency rate, network bandwidth and heterogeneity
which mainly depends on factors like code to be offloaded, distance between
smartphone device and cloud, wireless networks and complex computations. Final
output is traced back to the client (smartphone device) and thus saves the resources of
the smartphone device
2.4 Review of Offloading Approaches in Mobile
CloudComputing:
Meng-Hsi Chen†, Ben Liang†, Min Dong‡ [1] : This paper proposes an well
planned model with new offloading algorithm by semidefinate relaxation and a novel
randomization mapping method. It consisted of the mobile computing scenario in which
there are multiple independent tasks and one computing access point (CAP) along with
one remote server. The access point can either compute the received tasks from the user
or offloads them to the cloud .It improves the offloading decision of the user by
minimizing weighted total cost of energy ,computation and delay to optimal offloading
of tasks to the cloud by the user. The simulation results of the proposed model shows
that it improves the performance with only small number of randomization iterations
and when CAPs and a remote server is included it adds beneficial features in the
traditional
mobile
computing
system
and
improves
computation
performance.computing system and improves computation performance. Feng Xia ·
Fangwei Ding · Jie Li · Xiangjie Kong ·Laurence T. Yang · Jianhua Ma [2] : In this
paper we device a Phone2Cloud ,a computation offloading system which offloads the
computation of the application running on the mobile the user is using to the cloud and
hence improving the energy efficiency of the smartphone and enhancing the
performance of the application by reducing its execution time . It uses three key
methods in the system proposed including CPU workload prediction in the resource
monitor,bandwidth prediction in the bandwidth monitor and offloading decision
making algorithm. The decision making for offloading is important as to decide whether
the computation of application should or shouldn’t be offloaded to cloud to save energy
and application performance is improved. The energy efficient Phone2Cloud system
proposed is semi-automatic uses two sets of experiments to prove the effectiveness of
the proposed system and also takes advantage of computation offloading paradigm .
3
CHAPTER 3
SYSTEM DESIGN
3.1 EXISTING SYSTEM
Clinically, several heuristic approaches, such as “ABCD” rule
[6], Menzies method [7] and “CASH” [8], have been developed to enhance
clinicians’ ability to distinguish melanomas from benign nevi. However, the
correct diagnosis of a skin lesion is not trivial even for experienced
professionals. Furthermore, dermoscopic diagnosis made by human visual
inspection is often laborious, time-consuming and subjective. Hence,
unsatisfactory accuracy and poor reproducibility are still issues for diagnosing
this disease. To tackle these issues, numerous algorithms were proposed for
automatic dermoscopic image analysis. Interested readers can refer to [3, 9,
10] for a comprehensive summary of related work over the past decades. By
and large, the pipeline of these computer-aided analysis models usually
includes the following four steps: i) image preprocessing such as hair removal
[11-13] and image enhancement [14, 15]; ii) border detection or
segmentation [2, 16-18]; iii) feature extraction (i.e. color, texture, border
gradient, shape related descriptors) [2, 18-20]; iv) classification (k-nearest
neighbor (KNN) [18], support vector machine (SVM) [2], AdaBoost [20],
neural network [19], etc.). Most of the existing studies have mainly focused
on feature engineering and classification, either implicitly or explicitly,
assuming that the input image contains a lesion object in well-condition [19].
However, dermoscopy images may not always capture entire lesions, or
lesion object occupies only a small part of an image, as shown in Fig. 1.
Several studies proposed to adopt the bag-of-features (BoF) model with local
features
3.1.2 DISADVANTAGES

Inference time speed. For non trivial problems, you generally need a
very large network which can be extraordinarily time intensive to
evaluate at inference time.

Need a large dataset. Because of large dataset, training time is usually
significant.
4
3.2 PROPOSED SYSTEM
In order to improve the accuracy of feature extraction, eight
different preprocessing algorithms were used. The algorithms used were
converting to grey scale image, sharpening filter, median filter, smooth filter,
binary mask, RGB extraction, and histogram and sobel operator. The RGB
values of the images are extracted before converting it into a gray scale
image. Sharpening filter is applied to the gray scale image in order to
sharpen the details of the infected region. YCbCr was used to extract average
colour code of the infected area from the binary image.
3.2.1 ADVANTAGES
 Low computational complexity and time complexity compared to
existing method.
 High accuracy for all kinds of skin images.
5
CHAPTER-4
SYSTEM ARCHITECTURE
4.1.1 ARCHITECTURE DESCRIPTION
We propose a Finite Horizon Markov Decision Process
(FHMDP) to formulate this problem, with the aim to minimize
communication costs and satisfy delay constraints by offloading mobile data
as much as possible with WiFi network and D2D communication. Markov
decision process is a useful model for sequential decision making, where
MNO needs to take a sequence of actions (wireless network selection).
FHMDP is a Markov decision process with a finite number of decision
epochs . Since every data delivery task should be finished before a given
deadline, FHMDP will plan data offloading decisions at each decision epoch.
FHMDP planning phase can be implemented in remote cloud and ease the
heavy burden of complex data offloading management by MSthe coverage
area of base station. MHs can be considered as supplementary to WiFi APs
because of their mobi
6
CHAPTER-5
SYSTEM IMPLEMENTATION
5.1 Deep Residual Neural Network
The deep hierarchy architecture of CNN models is of crucial
importance for its powerful learning capability [28, 31]. In this work, we
adopt the latest generation of the convolutional neural network (deep
residual neural network, ResNet) introduced by He et al.[28], which is
ranked number one in ImageNet large-scale visual recognition challenge
2016 (ILSVRC 2016) for feature extraction. Compared with typical CNN
architectures, the main characteristic of ResNet lies in the adaptation of
residual connection which is capable of addressing the degradation
problem [28] when training a very deep network. It has been demonstrated
that the residual links can speed up the convergence of deep network and
maintain accuracy gains achieved by substantially increasing the network
depth. Generally, a deep residual network consists of a set of residual
blocks, and each block is composed of several stacked convolutional layers
5.2 Image Preprocessing and Data Augmentation
1) Image size: Since there is a huge variation in image resolutions
of the skin lesion dermoscopy dataset provided by ISBI 2016 challenge [5],
ranging from the largest scale (4288×2848) to the smallest scale (722×542).
Resizing and cropping these images directly into required size introduces
object distortion and substantial information loss [41, 43, 56]. In this study,
we take relatively large (compared with 224×224) as inputs. For the skin
lesion dataset, we resize these images along the shortest side to a uniform
scale (hereafter, we denote this scale as S for simplicity) while maintaining
the aspect ratio. We also investigate the recognition performance with
various values of S, and the results are presented in Section IV.
2) Image normalization and augmentation: Typically, before
processing by CNN, images are normalized by subtracting the mean pixel
value, which is calculated over the entire training dataset. As a result, the
RGB values are centered at zero (denoted as all-img-mean). However, the
lighting, skin tone and viewpoint of the skin lesion images vary greatly
across the dataset, subtracting a uniform mean value does not well
normalize the illumination of individual image.
7
5.3 Kernel-based Classification
For the classification of the FV representations, we train
an SVM classifier with Chi-squared (chi2) kernel. Although linear kernels
are efficient for the classification, non-linear kernels tend to yield better
performance and empirical studies have demonstrated the superiority of
the chi2 kernel for image classification [64, 65]. The homogeneity degree of
the kernel is set to 1 in our experiment. Note that the FV representations
are L2 normalized. In our experiment, we adopt the standard hinge loss in
the objective function and the parameter C use to scale the loss is fixed as 1.
During SVM training, the stochastic dual coordinate ascent algorithm
5.4 Modules Used
 Arduino uno
 Gsm
 Buzzer
 SYSTEM TYPE: 32bit / 64 bit
 RAM: >2GB
 OS: WINDOWS 7/8/8.1/10
 Microcontroller

LCD Display
A threshold limit was imposed on the Euler value heuristically,
exceeding which was an indicator of presence of a large number of inflictions.
This is an important distinguishing feature characteristic for diseases.
Segmentation separates the suspicious lesion from normal skin. There
are some unique features that distinguish the benign from malignant
melanoma.
8
The number of components of the skin affliction was extracted from
the image using the Euler value. For the classification we will use GLCM
(Gray Level Co-occurrence Matrix) and LBP (Local Binary Pattern).
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