Skin Lesion Analysis for Melanoma Detection Using the Novel Deep Learning Model Fuzzy GC-SCNN

Bhimavarapu, Usharani; Battineni, Gopi

doi:10.3390/healthcare10050962

Cited by 31 publications

(12 citation statements)

References 59 publications

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“…The relation between a rocket’s engine and the enormous amount of fuel used for a successful mission can represent the relation between the deep learning model and the data size used for training. Generally, deep learning models have many hidden neurons for achieving high performance on complex tasks [ 36 ].…”

Section: Methodsmentioning

confidence: 99%

An Efficient Deep Learning-Based Skin Cancer Classifier for an Imbalanced Dataset

et al. 2022

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Efficient skin cancer detection using images is a challenging task in the healthcare domain. In today’s medical practices, skin cancer detection is a time-consuming procedure that may lead to a patient’s death in later stages. The diagnosis of skin cancer at an earlier stage is crucial for the success rate of complete cure. The efficient detection of skin cancer is a challenging task. Therefore, the numbers of skilful dermatologists around the globe are not enough to deal with today’s healthcare. The huge difference between data from various healthcare sector classes leads to data imbalance problems. Due to data imbalance issues, deep learning models are often trained on one class more than others. This study proposes a novel deep learning-based skin cancer detector using an imbalanced dataset. Data augmentation was used to balance various skin cancer classes to overcome the data imbalance. The Skin Cancer MNIST: HAM10000 dataset was employed, which consists of seven classes of skin lesions. Deep learning models are widely used in disease diagnosis through images. Deep learning-based models (AlexNet, InceptionV3, and RegNetY-320) were employed to classify skin cancer. The proposed framework was also tuned with various combinations of hyperparameters. The results show that RegNetY-320 outperformed InceptionV3 and AlexNet in terms of the accuracy, F1-score, and receiver operating characteristic (ROC) curve both on the imbalanced and balanced datasets. The performance of the proposed framework was better than that of conventional methods. The accuracy, F1-score, and ROC curve value obtained with the proposed framework were 91%, 88.1%, and 0.95, which were significantly better than those of the state-of-the-art method, which achieved 85%, 69.3%, and 0.90, respectively. Our proposed framework may assist in disease identification, which could save lives, reduce unnecessary biopsies, and reduce costs for patients, dermatologists, and healthcare professionals.

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Section: Methodsmentioning

confidence: 99%

An Efficient Deep Learning-Based Skin Cancer Classifier for an Imbalanced Dataset

et al. 2022

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“…To augment the dataset, new images were created by altering spatial characteristics, including horizontal and vertical flips, rotations, changes in image brightness, and shifts in both horizontal and vertical directions, and adjusting the magnification of existing images. DL models, with their numerous hidden neurons, depend on both the diversity and the volume [97] of the dataset utilized in training to attain high efficiency in intricate tasks [98,99]. Furthermore, data augmentation is beneficial for simulating real-world applications, as it allows capturing images from various angles and perspectives, occasionally even in inverted forms, under different conditions and using varying camera specifications.…”

Section: Datasetmentioning

confidence: 99%

Improving the Concrete Crack Detection Process via a Hybrid Visual Transformer Algorithm

Shahin,

Chen,

Maghanaki

et al. 2024

Sensors

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Inspections of concrete bridges across the United States represent a significant commitment of resources, given their biannual mandate for many structures. With a notable number of aging bridges, there is an imperative need to enhance the efficiency of these inspections. This study harnessed the power of computer vision to streamline the inspection process. Our experiment examined the efficacy of a state-of-the-art Visual Transformer (ViT) model combined with distinct image enhancement detector algorithms. We benchmarked against a deep learning Convolutional Neural Network (CNN) model. These models were applied to over 20,000 high-quality images from the Concrete Images for Classification dataset. Traditional crack detection methods often fall short due to their heavy reliance on time and resources. This research pioneers bridge inspection by integrating ViT with diverse image enhancement detectors, significantly improving concrete crack detection accuracy. Notably, a custom-built CNN achieves over 99% accuracy with substantially lower training time than ViT, making it an efficient solution for enhancing safety and resource conservation in infrastructure management. These advancements enhance safety by enabling reliable detection and timely maintenance, but they also align with Industry 4.0 objectives, automating manual inspections, reducing costs, and advancing technological integration in public infrastructure management.

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“…On the publicly available PH2 dermoscopy imaging dataset, the method was tested, achieving 97.5%, 96.67%, and 100.0% for diagnostic accuracy, sensitivity, and specificity, respectively, which compared favorably with those obtained from several state-of-the-art approaches. In a more recent study [ 18 ], the authors present an innovative skin lesion analysis technique for melanoma detection using a fuzzy deep learning GrabCut-Stacked Convolutional Neural Network (GC-SCNN) model, where deep learning algorithms [ 19 ] are incorporated and the fuzzy GC-SCNN model is applied for image training to automatically detect melanoma in dermoscopic images. Skin lesion features are extracted and classified from different publicly available dermoscopic image datasets.…”

Section: Related Workmentioning

confidence: 99%

A Multi-Feature Fusion Framework for Automatic Skin Cancer Diagnostics

et al. 2023

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Malignant melanoma is the most invasive skin cancer and is currently regarded as one of the deadliest disorders; however, it can be cured more successfully if detected and treated early. Recently, CAD (computer-aided diagnosis) systems have emerged as a powerful alternative tool for the automatic detection and categorization of skin lesions, such as malignant melanoma or benign nevus, in given dermoscopy images. In this paper, we propose an integrated CAD framework for rapid and accurate melanoma detection in dermoscopy images. Initially, an input dermoscopy image is pre-processed by using a median filter and bottom-hat filtering for noise reduction, artifact removal, and, thus, enhancing the image quality. After this, each skin lesion is described by an effective skin lesion descriptor with high discrimination and descriptiveness capabilities, which is constructed by calculating the HOG (Histogram of Oriented Gradient) and LBP (Local Binary Patterns) and their extensions. After feature selection, the lesion descriptors are fed into three supervised machine learning classification models, namely SVM (Support Vector Machine), kNN (k-Nearest Neighbors), and GAB (Gentle AdaBoost), to diagnostically classify melanocytic skin lesions into one of two diagnostic categories, melanoma or nevus. Experimental results achieved using 10-fold cross-validation on the publicly available MED-NODEE dermoscopy image dataset demonstrate that the proposed CAD framework performs either competitively or superiorly to several state-of-the-art methods with stronger training settings in relation to various diagnostic metrics, such as accuracy (94%), specificity (92%), and sensitivity (100%).

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Skin Lesion Analysis for Melanoma Detection Using the Novel Deep Learning Model Fuzzy GC-SCNN

Cited by 31 publications

References 59 publications

An Efficient Deep Learning-Based Skin Cancer Classifier for an Imbalanced Dataset

An Efficient Deep Learning-Based Skin Cancer Classifier for an Imbalanced Dataset

Improving the Concrete Crack Detection Process via a Hybrid Visual Transformer Algorithm

A Multi-Feature Fusion Framework for Automatic Skin Cancer Diagnostics

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