{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T04:53:41Z","timestamp":1777697621917,"version":"3.51.4"},"reference-count":18,"publisher":"SAGE Publications","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IDT"],"published-print":{"date-parts":[[2024,2,20]]},"abstract":"<jats:p>The early diagnosis of skin cancer is of paramount importance for effective patient treatment. Dermoscopy, a non-surgical technique, utilizes precise equipment to examine the skin and plays a crucial role in identifying specific features and patterns that may indicate the presence of skin carcinoma. In recent times, machine learning (ML) methods have been developed to recognize and classify dermoscopic images as either malignant or benign. Deep learning (DL) systems, including Convolutional Neural Networks (CNNs), as well as various ML models like Random Forest (RF) classifiers and Support Vector Machine (SVM), are employed to extract relevant features from these images. This study introduces the Crow Search Algorithm with Deep Transfer Learning Driven Skin Lesion Detection on Dermoscopic Images (CSADTL-SLD) technique. The CSADTL-SLD method starts with the application of a median filter (MF) to remove noise from the images and utilizes the GoogleNet model for feature extraction. GoogleNet is well-regarded for its capacity to capture intricate and meaningful patterns within the data, which are essential for accurate lesion characterization. Furthermore, the CSADTL-SLD technique applies the Crow Search Algorithm (CSA) for parameter tuning of the GoogleNet model. After feature selection, the system employs the MLP classification model for precise lesion categorization. The comprehensive results of this research demonstrate the superiority of the CSADTL-SLD algorithm, showing significant enhancements in skin lesion detection accuracy and robustness when compared to existing methods. This approach holds promise as an effective solution for automating the detection and classification of skin lesions in dermoscopic images.<\/jats:p>","DOI":"10.3233\/idt-230307","type":"journal-article","created":{"date-parts":[[2024,2,2]],"date-time":"2024-02-02T10:46:21Z","timestamp":1706870781000},"page":"417-426","source":"Crossref","is-referenced-by-count":3,"title":["Crow search algorithm with deep transfer learning driven skin lesion detection on dermoscopic images"],"prefix":"10.1177","volume":"18","author":[{"given":"Awanish Kumar","family":"Mishra","sequence":"first","affiliation":[{"name":"Computer Science and Engineering, Pranveer Singh Institute of Technology, Kanpur, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tarun Dhar","family":"Diwan","sequence":"additional","affiliation":[{"name":"Information Technology, Government E R.R. Post Graduate Science College, Bilaspur, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Indresh Kumar","family":"Gupta","sequence":"additional","affiliation":[{"name":"Computer Science and Engineering, Pranveer Singh Institute of Technology, Kanpur, India"},{"name":"COPELABS, Lus\u00f3fona University, Lisbon, Portugal"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sonu","family":"Agrawal","sequence":"additional","affiliation":[{"name":"Computer Science and Engineering, Shri Shankaracharya Technical Campus, Bhilai, India"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","reference":[{"key":"10.3233\/IDT-230307_ref1","doi-asserted-by":"crossref","first-page":"100282","DOI":"10.1016\/j.imu.2019.100282","article-title":"Skin cancer detection: Applying a deep learning based model driven architecture in the cloud for classifying dermal cell images","volume":"18","author":"Kadampur","year":"2020","journal-title":"Inform. Med. 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