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Deep ensemble model for skin cancer classification with improved feature set

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Abstract

Early identification of skin cancer is mandatory to minimize the worldwide death rate as this disease is covering more than 30% of mortality rates in young and adults. Researchers are in the move of proposing advanced models for the accurate classification of skin disease. This paper introduces a novel skin cancer detection model under the stages of preprocessing, feature extraction, classification as well as segmentation. The given input is first preprocessed via Gaussian filtering. Subsequently, the Deep learning model termed as ‘U-net’ is used for the segmentation process. Afterward, the extraction of the feature set including features like Improved Center symmetric Local Binary Pattern (ICS-LBP), Local Gradient Pattern (LGP), Median binary pattern (MBP) is performed. Classification takes place using Deep Ensemble Model based on the extracted features, that includes BiGRU, Deep Maxout, and Improved Deep Convolutional Neural Network (CNN), respectively. Also, as the hyperparameter tuning of the classification model enhances the performance, this work introduces a new Hybrid Atom and Arithmetic Operation Algorithm (HAAOA) algorithm for tuning the optimal weights of the Bi-GRU (Bi-directional Gated Recurrent Unit) and Deep Maxout model. At last, the validation of the proposed work is performed through different performance measures by comparing it with different algorithms. Accordingly, accuracy of the adopted method at 90th learning percentage is 93.56%, which is ( ~) 82.63%, 85.15%, 84.98%, 83.66% and 83.42% better than AOA, ASO, BWO, BMO and DO, respectively.

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Data Availability

“The dermoscopic images were obtained at the Dermatology Service of Hospital Pedro Hispano (Matosinhos, Portugal) under the same conditions through Tuebinger Mole Analyzer system using a magnification of 20x. They are 8-bit RGB color images with a resolution of 768 × 560 pixels.

This image database contains a total of 200 dermoscopic images of melanocytic lesions, including 80 common nevi, 80 atypical nevi, and 40 melanomas. The PH2 database includes medical annotation of all the images namely medical segmentation of the lesion, clinical and histological diagnosis and the assessment of several dermoscopic criteria (colors; pigment network; dots/globules; streaks; regression areas; blue-whitish veil).”

Abbreviations

BCC:

Basal Cell Carcinoma

CAD:

Computer Aided Diagnosis

CAL:

Class Attention Layer

CapsNet:

Capsule Network

DCNN:

Deep Convolutional Neural Network

DEG:

Differentially Expressed Gene

DL:

Deep Learning

DLCAL-SLDC:

Deep Learning with a Class Attention Layer Based CAD Model for Skin Lesion Detection and Classification

GRU:

Gated Recurrent Unit

MCS:

Multi-Class Skin

MetaBlock:

Metadata Processing Block

KNN:

K-Nearest Neighbor

MF:

Median Filtering

NN:

Neural Network

RNN:

Recurrent Neural Network

SCC:

Squamous Cell Carcinoma

SGD:

Stochastic Gradient Decent

SpaSA:

Sparrow Search Algorithm

SVM:

Support Vector Machines

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Don Bosco Institute of Technology, Bengaluru, Karnataka, 560074, India

    Sreedhar Burada & B. E. Manjunathswamy

  2. Department of Computer Science and Engineering, School of Computing, Mohan Babu University, Sree Vidyanikethan Engineering College, Tirupati, Andhra Pradesh, 517102, India

    M. Sunil Kumar

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All authors have made substantial contributions to conception and design, revising the manuscript, and the final approval of the version to be published. Also, all authors agreed to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

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Burada, S., Manjunathswamy, B.E. & Kumar, M.S. Deep ensemble model for skin cancer classification with improved feature set. Multimed Tools Appl (2024). https://doi.org/10.1007/s11042-024-19039-5

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