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King Saud Univ. Comput. Inf. Sci."],"published-print":{"date-parts":[[2025,6]]},"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:p>Hyperparameter optimization (HPO) is essential for deep learning in medical image classification, yet standard metaheuristics such as Manta Ray Foraging Optimization (MRFO) often suffer from premature convergence in high-dimensional search spaces. To address these limitations, an enhanced variant, MRFO-LF, was proposed by incorporating L\u00e9vy flight-based exploration, adaptive step-size decay, and a hybrid stochastic\u2013deterministic search mechanism. This work details the first application of the proposed MRFO-LF to HPO in melanoma classification, a critical task within medical image analysis. The L\u00e9vy component enables long-range perturbations, while the adaptive decay mechanism gradually narrows the search scope, and the hybrid strategy balances global versus local exploration without relying on problem-specific heuristics. Experiments were conducted on the ISIC and PH<jats:inline-formula>\n              <jats:alternatives>\n                <jats:tex-math>$$ ^2 $$<\/jats:tex-math>\n                <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                  <mml:mmultiscripts>\n                    <mml:mrow\/>\n                    <mml:mrow\/>\n                    <mml:mn>2<\/mml:mn>\n                  <\/mml:mmultiscripts>\n                <\/mml:math>\n              <\/jats:alternatives>\n            <\/jats:inline-formula> dermoscopic datasets using DenseNet121, InceptionV3, and VGG19. MRFO-LF attained peak validation accuracies of 99.49% (ISIC) and 100.00% (PH<jats:inline-formula>\n              <jats:alternatives>\n                <jats:tex-math>$$ ^2 $$<\/jats:tex-math>\n                <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                  <mml:mmultiscripts>\n                    <mml:mrow\/>\n                    <mml:mrow\/>\n                    <mml:mn>2<\/mml:mn>\n                  <\/mml:mmultiscripts>\n                <\/mml:math>\n              <\/jats:alternatives>\n            <\/jats:inline-formula>) for DenseNet121, with corresponding validation losses of 0.3580 and 0.0015. When compared to MRFO, PSO, and GA, the proposed method improved ISIC accuracy by 0.40%, reduced PH<jats:inline-formula>\n              <jats:alternatives>\n                <jats:tex-math>$$ ^2 $$<\/jats:tex-math>\n                <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                  <mml:mmultiscripts>\n                    <mml:mrow\/>\n                    <mml:mrow\/>\n                    <mml:mn>2<\/mml:mn>\n                  <\/mml:mmultiscripts>\n                <\/mml:math>\n              <\/jats:alternatives>\n            <\/jats:inline-formula> loss by over 95%, and converged up to 30% faster. Statistical significance was confirmed through ANOVA and paired t-tests (<jats:inline-formula>\n              <jats:alternatives>\n                <jats:tex-math>$$ p &lt; 0.05 $$<\/jats:tex-math>\n                <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                  <mml:mrow>\n                    <mml:mi>p<\/mml:mi>\n                    <mml:mo>&lt;<\/mml:mo>\n                    <mml:mn>0.05<\/mml:mn>\n                  <\/mml:mrow>\n                <\/mml:math>\n              <\/jats:alternatives>\n            <\/jats:inline-formula>). These results position MRFO-LF as a reliable and efficient optimizer for complex hyperparameter tuning in medical image classification.<\/jats:p>","DOI":"10.1007\/s44443-025-00078-3","type":"journal-article","created":{"date-parts":[[2025,6,12]],"date-time":"2025-06-12T15:32:10Z","timestamp":1749742330000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Adaptive hybrid hyperparameter optimization with MRFO and L\u00e9vy flight for accurate melanoma classification"],"prefix":"10.1007","volume":"37","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-4015-1723","authenticated-orcid":false,"given":"Shamsuddeen","family":"Adamu","sequence":"first","affiliation":[]},{"given":"Hitham","family":"Alhussian","sequence":"additional","affiliation":[]},{"given":"Said Jadid","family":"Abdulkadir","sequence":"additional","affiliation":[]},{"given":"Ayed","family":"Alwadin","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2845-5675","authenticated-orcid":false,"given":"Sallam O.\u00a0F.","family":"Khairy","sequence":"additional","affiliation":[]},{"given":"Hussaini","family":"Mamman","sequence":"additional","affiliation":[]},{"given":"Shamsu","family":"Abdullahi","sequence":"additional","affiliation":[]},{"given":"Saidu","family":"Yahaya","sequence":"additional","affiliation":[]},{"given":"Aliyu","family":"Garba","sequence":"additional","affiliation":[]},{"given":"Dahiru Adamu","family":"Aliyu","sequence":"additional","affiliation":[]},{"given":"Muhammad Muntasir","family":"Yakubu","sequence":"additional","affiliation":[]},{"given":"Daniel Tonye","family":"Oyefidein","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,6,12]]},"reference":[{"key":"78_CR1","doi-asserted-by":"crossref","unstructured":"Abdel-Salam M, Hu G, \u00c7elik E, Gharehchopogh F\u00a0S, EL-Hasnony I\u00a0M (2024) Chaotic RIME optimization algorithm with adaptive mutualism for feature selection problems. 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