Modified U-NET Architecture for Segmentation of Skin Lesion

Anand, Vatsala; Gupta, Sheifali; Koundal, Deepika; Nayak, Soumya Ranjan; Barsocchi, Paolo; Bhoi, Akash Kumar

doi:10.3390/s22030867

Cited by 110 publications

(12 citation statements)

References 41 publications

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“…“Categorical cross-entropy” is used as a loss function in the experiment. We also used the learning rate reduction function to reduce the learning rate as the epoch increases [ 106 ].…”

Section: Experiments and Resultsmentioning

confidence: 99%

A Secure Framework toward IoMT‐Assisted Data Collection, Modeling, and Classification for Intelligent Dermatology Healthcare Services

Islam

Kaushal

Amin

et al. 2022

Contrast Media & Molecular Imaging

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The abnormal growth of the skin cells is known as skin cancer. It is one of the main problems in the dermatology area. Skin lesions or malignancies have been a source of worry for many individuals in recent years. Irrespective of the skin tone, there exist three major classes of skin lesions, i.e., basal cell carcinoma, squamous cell carcinoma, and melanoma. The early diagnosis of these lesions is equally important for human life. In the proposed work, a secure IoMT-Assisted framework is introduced that can help the patients to do the initial screening of skin lesions remotely. The initially proposed approach uses an IoMT-based data collection device which is accessible by patients to capture skin lesions images. Next, the captured skin sample is encrypted and sent to the collected image toward cloud storage. Later, the received sample image is classified into appropriate class labels using an ensemble classifier. In the proposed framework, four CNN models were ensemble i.e., VGG-16, DenseNet-201, Inception-V3, and Efficient-B7. The framework has experimented with the “HAM10000” dataset having 7 different kinds of skin lesions data. Although DenseNet-201 performed well, the ensemble model provides the highest accuracy with 87.22 percent as well as its test loss/error is lower than others with 0.4131. Moreover, the ensemble model’s classification ability is much higher with an AUC score of 0.9745. Moreover, A recommendation team has been assigned to assess the sample of the patient as well as suggest the patient according to classified results by the CAD.

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“…“Categorical cross-entropy” is used as a loss function in the experiment. We also used the learning rate reduction function to reduce the learning rate as the epoch increases [ 106 ].…”

Section: Experiments and Resultsmentioning

confidence: 99%

A Secure Framework toward IoMT‐Assisted Data Collection, Modeling, and Classification for Intelligent Dermatology Healthcare Services

Islam

Kaushal

Amin

et al. 2022

Contrast Media & Molecular Imaging

View full text Add to dashboard Cite

show abstract

“…Classification is necessary since it necessitates the application of judgement at every level. An SVM’s performance can be improved by first gaining control over it and then adapting it to the results desired by the algorithm ( Ahmad et al, 2021 ; Anand et al, 2022a ; Anand et al, 2022b ; Anand et al, 2022c ; Anand et al, 2022d ; Hossen et al, 2022 ; Krishnamoorthi et al, 2022 ). At each moment in time, feature vectors can undergo dynamic transitions between x-activities.…”

Section: Proposed Methodsmentioning

confidence: 99%

Early and accurate detection of melanoma skin cancer using hybrid level set approach

et al. 2022

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Digital dermoscopy is used to identify cancer in skin lesions, and sun exposure is one of the leading causes of melanoma. It is crucial to distinguish between healthy skin and malignant lesions when using computerised lesion detection and classification. Lesion segmentation influences categorization accuracy and precision. This study introduces a novel way of classifying lesions. Hair filters, gel, bubbles, and specular reflection are all options. An improved levelling method is employed in an innovative method for detecting and removing cancerous hairs. The lesion is distinguished from the surrounding skin by the adaptive sigmoidal function; this function considers the severity of localised lesions. An improved technique for identifying a lesion from surrounding tissue is proposed in the article, followed by a classifier and available features that resulted in 94.40% accuracy and 93% success. According to research, the best method for selecting features and classifications can produce more accurate predictions before and during treatment. When the recommended strategy is put to the test using the Melanoma Skin Cancer Dataset, the recommended technique outperforms the alternative.

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“…Anand et al presented an automated approach for the segmentation of skin lesions using a modified U-Net architecture. 32 More kernels were added to the network for precise feature extraction. A comparative analysis of the existing works applied for segmentation of skin diseases is presented in Table 2.…”

Section: Segmentation Techniquesmentioning

confidence: 99%

A novel two‐staged network for skin disease detection using atrous residual convolutional networks

Ramamurthy,

Muthuswamy,

Mathimariappan

et al. 2023

Concurrency and Computation

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SummaryThe preferred modality for the identification of skin disease has predominantly been dermoscopy. Skin lesion identification is the primary diagnostic basis for the dermatologist to evaluate the severity and impact of the disease. Current diagnostic procedures for the identification of skin lesions may be subjected to misdiagnosis among observers. Also, these procedures are conventionally labour‐intensive and prone to delays in treatment. Deep learning has proven to be beneficial for automated computer‐aided diagnosis in the medical field. This research presents a two‐stage approach involving segmentation and classification architectures for the effective detection of skin disease. The proposed approach aims to combine the architectural benefits of residual learning with the contextual retention of atrous convolutions. The classification is performed on seven different classes of skin lesions namely, melanoma (MEL), melanocytic nevus (NV), basal cell carcinoma (BCC), actinic keratosis/Bowen's disease (AKIEC), benign keratosis (BKL), dermatofibroma (DF), and vascular lesion (VASC). To the best of our knowledge, this is the first research that reports the impact of a two‐staged detection network using atrous residual convolutions for these seven classes. Experimental observations indicate that the proposed model is characteristically balanced in terms of inter‐class performance, with precise segmentation. The proposed network was trained with the HAM10000 dataset and reported a classification accuracy and precision of 89.27% and 89.06%, respectively.

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Modified U-NET Architecture for Segmentation of Skin Lesion

Cited by 110 publications

References 41 publications

A Secure Framework toward IoMT‐Assisted Data Collection, Modeling, and Classification for Intelligent Dermatology Healthcare Services

A Secure Framework toward IoMT‐Assisted Data Collection, Modeling, and Classification for Intelligent Dermatology Healthcare Services

Early and accurate detection of melanoma skin cancer using hybrid level set approach

A novel two‐staged network for skin disease detection using atrous residual convolutional networks

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