Deep Learning-Based Skin Lesion Detection and Classification: A Review

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 09 Issue: 07 | July 2022 www.irjet.net p-ISSN: 2395-0072
© 2022, IRJET | Impact Factor value: 7.529 | ISO 9001:2008 Certified Journal | Page 1366
Deep Learning-Based Skin Lesion Detection and Classification:
A Review
Niharika S 1, Dr. Bhanushree K J 2
1 Department of Computer Science and Engineering, Bangalore Institute of Technology, Bengaluru, India
2Assistant Professor, Department of Computer Science and Engineering, Bangalore Institute of Technology,
Bengaluru, India
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Abstract - Detection and classification of skin lesions are
crucial in diagnosing skin cancer and detecting melanoma.
Melanoma is a menacing form of skin cancer accountable for
taking the lives of numerous people each year. Early
identification of melanoma isessentialandattainablethrough
visual examination of pigmentedlesionsontheskin, treated by
extirpating the cancerous cells. Standard vision detection of
melanoma in skin lesion images mightbeimprecise. Thevisual
similarity between the benign and malignant types poses
hardship in identifying melanoma. To solve the problems in
identifying melanoma, automated models are neededtoassist
dermatologists in the identificationtask. Thispaperpresentsa
comprehensive review and analysis of the various deep
learning techniques used to diagnose and classify skin lesions.
Key Words: Skin cancer, skin lesion detection and
classification, deep learning, image processing,
Convolution Neural Network, Fuzzy neural network.
1. INTRODUCTION
Skin lesions are skin portions withanatypical appearanceor
growth in contrast to the surrounding skin. Skin melanoma
is a type of deadly skin cancer. The epidermis is one of the
many layers of human skin, producing melanocytes that
produce melanin at a high rate. Prolonged exposure to the
sun's UV rays produces melanin.Theabnormal development
of melanocytes leads to melanoma, a cancerous tumour, the
deadliest skin cancer. Early diagnosis of melanoma is
essential for planningtreatmentandsavingtheaffected.This
is achievable by visual observationof pigmentedskinlesions
healed by simply removing the cancer cells. Detecting
melanoma from images of skin lesions using human vision
can be inaccurate. The stark resemblance between benign
and malignant types poses hardship in differentiating
between them and identifying melanoma. Also, traditional
methods like biopsy are time-consuming, painful and
expensive. Therefore, an automated computer model that
supports specialists in identification tasks is essential. In
recent times, deep learning techniques are frequently used
skin lesion detection. It is considered a class of machine
learning that utilises several layers to extricate complex-
level features from the input. Since a considerableamount of
research has been done regardingskinlesiondetectionusing
deep learning techniques. It's vital to survey and summarise
the research findings for future researchers. This paper
reviews the different deep learning techniques used, like
convolution neural networks and artificial neural networks
for skin lesion detection.
2. LITERATURE REVIEW
M. Kahn et al. [1] proposed a fully automated system
classifying skin lesions into many classes. They describe
segmentation techniques using deep learning and CNN
feature optimisation using an enhanced Moth Flame
Optimization (IMFO) method as partoftheframework.First,
the input image is stretched with the Histogram Intensity
Value with Local Color Key (LCcHIV). Subsequently,saliency
is evaluated using a new deep saliency segmentation
technique using a 10-layer convolutional neural network. A
pre-trained CNN is used for feature extraction from the
segmented colour lesion images. They proposed an
improved Moth Flame Optimization (IMFO) algorithm to
choose the mostdiscriminatingfeatures.TheKernel Extreme
Learning Machine (KELM) classifies the features. The
limitation of this task is the increase in calculation time. In
addition, advanced segmentation techniques are needed to
avoid deep model training on irrelevant image features.
P. Dhar et al. [2] put forward a technique for segmentation
and detecting skin lesions utilising dermoscopy images. The
proposed method is based on fuzzy logic and classification
rules using CNN. First, a set of rules is adapted to the
dermoscopy image. The output is thresholded. The close
operation is used as a morphological tool on threshold
images. Area filtering is then performed to generate the
desired area. For classification, CNN was used. The dataset
under consideration is inadequate and unbalanced.
Classification of images without skin lesions gave poor
results.
M. Arshad et al. [3] presented a novel automated framework
for classifying multiclass skin lesions. The pre-processing
involves three operations: 90 rotations, flip left / right and
flip-up / down. Next, the deep model is fine-tuned.ResNet50
and ResNet101 are the two selected models, andtheirlayers
are updated. In addition, transfer learning is applied,
features are extricated, and fusion is performed using an
altered series-based method. The final selected feature is
categorised using multiple machinelearningalgorithms.The
fusion system's limitations include an increase in

computation time. The results demonstrate that the feature
selection procedure decreases calculation time and
precision.
M.A. Kahn et al. [4] proposed an approach to emphasise
segmentation and lesion classification using deep CNN. The
proposed Gauss's method is used to improve contrast in the
first phase, then RGB to HSV colour space conversion,
followed by a prominencemapoflesionsegmentation.DCNN
functionality is retrieved through two distinct levels and
combined using a concurrent, decision-drivenmethodology.
Then, the top features are chosen and given to the ANN for
categorization. The methodology follows four basic steps:
lesion pretreatment, prominent segmentation detection,
feature extraction with optimum feature choice, deep
learning, and final categorization of neural networks. The
drawback of the proposed method is that it relies somewhat
on pretreatment steps (contrast stretch). Additional
calculations are made when you add pre-processing and
segmentation steps. Decreasing the number of predictors
will likely reduce performance, especially for complex
dermoscopy images.
Nida et al. [5] proposed novel a technique based on Fuzzy C-
mean (FCM) clustering and Deep Area-Based Convolutional
Neural Network (RCNN) for effective Melanoma region
segmentation inside dermoscopic pictures. The method
consists of three steps: Skin refinement usingmorphological
closing operation. Detection and localisation of Melanoma
using RCNN. Segmentation of Melanoma using Fuzzy C-
Means. This proposed system does not considerskindisease
classification as it includes only detection.
L Bi et al. [6] A fully convolutional network (FCN) has been
suggested that automatically segments skin lesions while
recognising objects by hierarchically mixinglow-level visual
input with high-level semantic information. A novel parallel
integration technique obtained final segmentation results
with accurate location and clear lesion boundaries.
Methodology: Robustness results from multi-level FCNs
iteratively learningandinferringcomplexskinlesions'visual
characteristics, always minimising segmentation errors in
training and test times. Additionally, the use of parallel
integration to incorporate supplementary data fromvarious
stages of mFCN has made it possible to constantly recognise
complex skin lesions' complex boundaries. One limitation is
that this proposed system does not consider the
classification of skin diseases.
In this paper, Mohammad Ali Kadanpur et al. [7] utilised a
deep learning architecture that is model-driven. This white
paper described the DLS tool's features and demonstrated
how to use it to create a deep learning model. The method of
data preparation employing skin cell pictures and their test
application in the DLS model for cancer cell detection were
both covered in this research. The DLS model successfully
identified cancer cells from cancer cell pictures with an AUC
of 99.77 per cent. Methodology: DLS, a model-driven
architecture tool, offers components for building neural
networks as a drag-and-drop art stack. Theessential general
procedure sequence included as a research methodology in
this document is data preparation, project creation, and
loading. Publish datasets, deep learning classifier creation,
model tuning, result validation, inference drawing, code
access, and models as REST APIs. This paperpointedout the
offer to receive. The model's source code is provided for
programmers to examine further. The best foundational
research this paper observes forfuturework isthecapability
to download trained models and create enterprise-level
applications. Finally, the objectives outlined in the
introduction section have been met by this research.
In this study, G. Reshma et al. [8] have developed a new
IMLTDL a deep learning-based automatic skin lesion
segmentation model for effective skin lesion segmentation
and an intelligent classification model for dermatoscopic
imaging. Methodology: The IMLTDT model diagnoses skin
lesions using various steps such as pretreatment, Feature
extraction, segmentation, and classification. The exhibited
IMLTDL model incorporates top hat filters at the base level
and repair techniques to preprocess dermoscopy images.
The infected skin lesion area in the dermoscopy image is
then identified using a multi-level threshold-based
segmentation.Effectiveskin lesiondetectionisaccomplished
using feature extraction procedures based on Inception v3
and classification processes based on GBT. The ISIC dataset
is used to run the proposedIMLTDLmodel andanalysesome
of the experimental findings.
M.Y. Sikkandar et al. [9] proposed a system that is a skin
lesion diagnosis segmentation-based classification model
that combines the Grab Cut algorithm and the Adaptive
NeuroFuzzy classifier(ANFC)model.Tophatfilteringisused
during the preprocessing stage. The morphological image
processing strategy, a top hat filter, is used to extractminute
details and elements from the provided photos in order to
detect the dense and dark hair present in the image.
Segmentation is carried out using the Grab cut algorithm to
segment the preprocessedimages.GrabCutisa methodology
for iterative,semi-automaticpicturesegmentationwherethe
segmentation of the image can be expressed as a graph. The
pixels from the image are represented by the created
network nodes. A deep learning-based Inception model is
used for the feature extractionprocess.Convolutional neural
network architecture known as Inceptionv4 improves on
earlier incarnations of the Inception family by streamlining
the design and utilising more Inception modules than
Inceptionv3. The dermoscopic pictures are then classified
into several classifications using an adaptive neuro-fuzzy
classifier (ANFC) method. The ANFC is a hybrid model that
combines the advantages of the NN's capacity for self-
adaptation and learning with the fuzzy model's capacity for
taking into account the present state of uncertainty and the
imprecision of real-time models. With the help of the ANFC
model, the fuzzy primary system is generated with the help
of filtered rules from the I / O data. Then use a neural

network to tune the rules of the primary model to generate
the final ANFC model. Other optimal techniques can be used
in the feature extraction step. In the future, you can improve
performance by using other deep learning models in
Inception v4.
3. CONCLUSIONS
A detailed review of the different deep learning techniques
used in skin lesion detection has been discussed. Eachpaper
has been analysed, and a summary of variousmethodologies
and their limitations have been included for futureresearch.
Some models used were fully Convolutional Neural
Networks, Artificial Neural Networks, Fuzzy C-Means, and a
hybrid model called the Adaptive Neuro-Fuzzy Classifier
model. Constraints in these papers were the increase in
computational time. Furthermore, extending the
segmentation technique is required to prevent deep models
from being trained on pointless image characteristics.
Additionally, a performance decreasewasnoted, particularly
when complicated dermoscopic pictures were involved.
Going forward, more enhanced and optimal segmentation
and feature extractions techniques are required. More
ensemble and hybrid classification models using deep
learning can be used for better results.
REFERENCES
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