<scp>SLDCNet</scp>: Skin lesion detection and classification using full resolution convolutional network‐based deep learning <scp>CNN</scp> with transfer learning

Varma, P. Bharat Siva; Paturu, Siddartha; Mishra, Suman; Rao, B. Srinivasa; Kumar, Pala Mahesh; Krishna, Namani Vamshi

doi:10.1111/exsy.12944

Cited by 21 publications

(9 citation statements)

References 39 publications

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“…A 3D self supervised quantum inspired NN for diagnosing medical data is introduced in [91], whereby MR brain images and liver tumor data is used, and a quantum fuzzy logic processes the information of low level and high level features of the local image in order to form accurate segmentation of the 3D medical data and obtain the greatest accuracy of 99% and 98.9% respectively. For cervical cancer classification, further quantum inspired weed optimisation with DL is described in [92]. A Gabor filtering is used during pre-processing, whereby features are extracted using a DCNN based on the SqueezeNet approach, and a deep variational autoencoder is used and maximum accuracy of 99.07 obtained%.…”

Section: A Medical Healthcare Record (Mhr)mentioning

confidence: 99%

Quantum Machine Learning Revolution in Healthcare: A Systematic Review of Emerging Perspectives and Applications

Ullah,

Garcia-Zapirain

2024

IEEE Access

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Quantum computing (QC) stands apart from traditional computing systems by employing revolutionary techniques for processing information. It leverages the power of quantum bits (qubits) and harnesses the unique properties exhibited by subatomic particles, such as superposition, entanglement, and interference. These quantum phenomena enable quantum computers to operate on an entirely different level, exponentially surpassing the computational capabilities of classical computers. By manipulating qubits and capitalising on their quantum states, QC holds the promise of solving complex problems that are currently intractable in the case of traditional computers. The potential impact of QC extends beyond its computational power and reaches into various critical sectors, including healthcare. Scientists and engineers are working diligently to overcome various challenges and limitations associated with QC technology. These include issues related to qubit stability, error correction, scalability, and noise reduction. In such a scenario, our proposed work provides a concise summary of the most recent state of the art based on articles published between 2018 and 2023 in the healthcare domain. Additionally, the approach follows the necessary guidelines for conducting a systematic literature review. This includes utilising research questions and evaluating the quality of the articles using specific metrics. Initially, a total of 2,038 records were acquired from multiple databases, with 468 duplicate records and 1,053 records unrelated to healthcare subsequently excluded. A further 258, 68, and 39 records were eliminated based on title, abstract, and fulltext criteria, respectively. Ultimately, the remaining 49 articles were subject to evaluation, thus providing a brief overview of the recent literature and contributing to existing knowledge and comprehension of Quantum Machine Learning (QML) algorithms and their applications in the healthcare sector. This analysis establishes a foundational framework for forthcoming research and development at the intersection of QC and machine learning, ultimately paving the way for innovative approaches to addressing complex challenges within the healthcare domain.INDEX TERMS Quantum computing, quantum machine learning algorithms, a systematic review, healthcare.

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Section: A Medical Healthcare Record (Mhr)mentioning

confidence: 99%

Quantum Machine Learning Revolution in Healthcare: A Systematic Review of Emerging Perspectives and Applications

Ullah,

Garcia-Zapirain

2024

IEEE Access

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“…The article by Esteva A. et al (Varma et al, 2022) discussed using a deep convolutional neural network to automatically classify skin lesions from clinical images. The CNN was trained with a dataset of 129,450 clinical images, featuring 2032 distinct diseases.…”

Section: Literature Surveymentioning

confidence: 99%

Optimizing time prediction and error classification in early melanoma detection using a hybrid RCNN‐LSTM model

Arjun,

Kumar,

Dhanaraj

et al. 2024

Microscopy Res & Technique

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Skin cancer is a terrifying disorder that affects all individuals. Due to the significant increase in the rate of melanoma skin cancer, early detection of skin cancer is now more critical than ever before. Malignant melanoma is one of the most serious forms of skin cancer, and it is caused by abnormal melanocyte cell growth. In recent years, skin cancer predictive categorization has become more accurate and predictive due to multiple deep learning algorithms. Malignant melanoma is diagnosed using the Recurrent Convolution Neural Network‐Long Short‐Term Memory (RCNN‐LSTM), which is one of the deep learning classification approaches. Using the International Skin Image Collection and the RCNN‐LSTM, the data are categorized and analyzed to gain a better understanding of skin cancer. The method begins with data preprocessing, which prepares the dataset for classification. Additionally, the RCNN is employed to extract the features that are vital to the prediction process. The LSTM is accountable for the final step, classification. There are further factors to examine, such as the precision of 94.60%, the sensitivity of 95.67%, and the F1‐score of 95.13%. Other benefits of the suggested study include shorter prediction durations of 95.314, 122.530, and 131.205 s and lower model loss of 0.25%, 0.19%, and 0.15% for input sizes 10, 15, and 20, respectively. Three datasets had a reduced categorization error of 5.11% and an accuracy of 95.42%. In comparison to previous approaches, the work discussed here produces superior outcomes.Research Highlights Recurrent convolutional neural network (RCNN) deep learning approach for optimizing time prediction and error classification in early melanoma detection. It extracts a high number of specific features from the skin disease image, making the classification process easier and more accurate. To reduce classification errors in accurately detecting melanoma, context dependency is considered in this work. By accounting for context dependency, the deprivation state is avoided, preventing performance degradation in the model. To minimize melanoma detection model loss, a skin disease image augmentation or regularization process is performed in this work. This strategy improves the accuracy of the model when applied to fresh, previously unobserved data.

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“…Researchers have attained many advancements using AI, mainly in finding patterns of diseases from medical imaging [17]. AI-based tools and applications in the field of dermatology are being designed to analyze the severity of psoriasis [18], and these AIbased tools involve the development of a computer algorithm that can self-learn specific dermatological tasks, such as classifying skin lesions as melanoma or nonmelanoma skin cancer [19,20]. Implementing federated learning-, deep learning-, and transfer learningbased technologies yields massive benefits for patients and dermatologists in predicting and diagnosing suspicious skin lesions.…”

Section: Introductionmentioning

confidence: 99%

Federated and Transfer Learning Methods for the Classification of Melanoma and Nonmelanoma Skin Cancers: A Prospective Study

Riaz,

Naeem,

Malik

et al. 2023

Sensors

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Skin cancer is considered a dangerous type of cancer with a high global mortality rate. Manual skin cancer diagnosis is a challenging and time-consuming method due to the complexity of the disease. Recently, deep learning and transfer learning have been the most effective methods for diagnosing this deadly cancer. To aid dermatologists and other healthcare professionals in classifying images into melanoma and nonmelanoma cancer and enabling the treatment of patients at an early stage, this systematic literature review (SLR) presents various federated learning (FL) and transfer learning (TL) techniques that have been widely applied. This study explores the FL and TL classifiers by evaluating them in terms of the performance metrics reported in research studies, which include true positive rate (TPR), true negative rate (TNR), area under the curve (AUC), and accuracy (ACC). This study was assembled and systemized by reviewing well-reputed studies published in eminent fora between January 2018 and July 2023. The existing literature was compiled through a systematic search of seven well-reputed databases. A total of 86 articles were included in this SLR. This SLR contains the most recent research on FL and TL algorithms for classifying malignant skin cancer. In addition, a taxonomy is presented that summarizes the many malignant and non-malignant cancer classes. The results of this SLR highlight the limitations and challenges of recent research. Consequently, the future direction of work and opportunities for interested researchers are established that help them in the automated classification of melanoma and nonmelanoma skin cancers.

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SLDCNet: Skin lesion detection and classification using full resolution convolutional network‐based deep learning CNN with transfer learning

Cited by 21 publications

References 39 publications

Quantum Machine Learning Revolution in Healthcare: A Systematic Review of Emerging Perspectives and Applications

Quantum Machine Learning Revolution in Healthcare: A Systematic Review of Emerging Perspectives and Applications

Optimizing time prediction and error classification in early melanoma detection using a hybrid RCNN‐LSTM model

Federated and Transfer Learning Methods for the Classification of Melanoma and Nonmelanoma Skin Cancers: A Prospective Study

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