{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,22]],"date-time":"2026-01-22T15:43:14Z","timestamp":1769096594269,"version":"3.49.0"},"reference-count":40,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2023,8,18]],"date-time":"2023-08-18T00:00:00Z","timestamp":1692316800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Int J Imaging Syst Tech"],"published-print":{"date-parts":[[2024,1]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Deep learning models, such as convolutional neural network (CNN), are popular now a day to solve various complex problems in medical and other fields, such as image classification, object detection, recommendation of images, processing of natural languages and video and image analysis. So, the idea of studying the architecture of CNNs has gotten a lot of attention and become popular. This study analysed and contrasted the performance of many different CNN models trained on the publicly accessible Br35h dataset for the detection of brain tumours. These models included the LeNet, AlexNet, VGG16, VGG19 and ResNet50. Several optimisers were used in this research to fine\u2010tune the performance of the CNN model. These included Adam (adaptive moment estimation), SGD (stochastic gradient descent) and RMSprop (root\u2010mean\u2010square propagation). Accuracy, miss\u2010classification rate, sensitivity, specificity, NPV (negative predictive value), PPV (positive predictive value), F1\u2010score and false omission rate (FOR) were used to assess the efficacy of five different CNN architectures trained using three different optimisers. The experimental results showed that AlexNet architecture with SGD optimiser performed better than other CNN architecture with different optimisers and achieved the highest accuracy of 98.79% with a miss classification rate of 1.20%. It also achieved 98.98% sensitivity, 98.58% specificity, 98.93% NPV, 98.65% PPV, 98.82% F1\u2010score and 1.06% FOR.<\/jats:p>","DOI":"10.1002\/ima.22949","type":"journal-article","created":{"date-parts":[[2023,8,21]],"date-time":"2023-08-21T19:16:48Z","timestamp":1692645408000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":28,"title":["Performance analysis of<scp>state\u2010of\u2010the\u2010art CNN<\/scp>architectures for brain tumour detection"],"prefix":"10.1002","volume":"34","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4386-1527","authenticated-orcid":false,"given":"Hafiz Muhammad Tayyab","family":"Khushi","sequence":"first","affiliation":[{"name":"Faculty of Computer Science &amp; Information Technology The Superior University Lahore Pakistan"},{"name":"Intelligent Data Visual Computing Research (IDVCR) Lahore Pakistan"}]},{"given":"Tehreem","family":"Masood","sequence":"additional","affiliation":[{"name":"Faculty of Computer Science &amp; Information Technology The Superior University Lahore Pakistan"},{"name":"Intelligent Data Visual Computing Research (IDVCR) Lahore Pakistan"}]},{"given":"Arfan","family":"Jaffar","sequence":"additional","affiliation":[{"name":"Faculty of Computer Science &amp; Information Technology The Superior University Lahore Pakistan"},{"name":"Intelligent Data Visual Computing Research (IDVCR) Lahore Pakistan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2321-3845","authenticated-orcid":false,"given":"Sheeraz","family":"Akram","sequence":"additional","affiliation":[{"name":"Faculty of Computer Science &amp; Information Technology The Superior University Lahore Pakistan"},{"name":"Intelligent Data Visual Computing Research (IDVCR) Lahore Pakistan"},{"name":"Information Systems Department, College of Computer and Information Sciences Imam Mohammad Ibn Saud Islamic University (IMSIU) Riyadh Saudi Arabia"}]},{"given":"Sohail Masood","family":"Bhatti","sequence":"additional","affiliation":[{"name":"Faculty of Computer Science &amp; Information Technology The Superior University Lahore Pakistan"},{"name":"Intelligent Data Visual Computing Research (IDVCR) Lahore Pakistan"}]}],"member":"311","published-online":{"date-parts":[[2023,8,18]]},"reference":[{"key":"e_1_2_10_2_1","doi-asserted-by":"publisher","DOI":"10.1136\/svn-2017-000101"},{"key":"e_1_2_10_3_1","doi-asserted-by":"crossref","unstructured":"AwangMBI IbrahimS.An overview of segmentation and classification techniques: a survey of brain tumour\u2010related research. 2nd Int. Conf Artif Intell Data Sci AiDAS.2021. doi:10.1109\/AIDAS53897.2021.9574170","DOI":"10.1109\/AiDAS53897.2021.9574170"},{"key":"e_1_2_10_4_1","doi-asserted-by":"publisher","DOI":"10.3390\/s22124426"},{"key":"e_1_2_10_5_1","doi-asserted-by":"publisher","DOI":"10.3390\/cancers11111673"},{"key":"e_1_2_10_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.procs.2021.12.053"},{"key":"e_1_2_10_7_1","doi-asserted-by":"publisher","DOI":"10.1093\/rpd\/ncq291"},{"key":"e_1_2_10_8_1","first-page":"1","article-title":"Intelligent model for brain tumor identification using deep learning","volume":"2022","author":"Khan AH","year":"2022","journal-title":"Appl Comput Intell Soft Comput"},{"key":"e_1_2_10_9_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-022-13166-7"},{"key":"e_1_2_10_10_1","doi-asserted-by":"publisher","DOI":"10.3390\/s22020523"},{"key":"e_1_2_10_11_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1155\/2022\/7348344","article-title":"Enhanced watershed segmentation algorithm\u2010based modified ResNet50 model for brain tumor detection","volume":"2022","author":"Sharma AK","year":"2022","journal-title":"Biomed Res Int"},{"key":"e_1_2_10_12_1","doi-asserted-by":"crossref","unstructured":"GuptaP ShuklaAP.Improving accuracy of lung nodule classification using AlexNet model.2021Proc. 2021 IEEE Int. Conf. Innov. Comput. Intell. Commun. Smart Electr. Syst. ICSES 2021. doi:10.1109\/ICSES52305.2021.9633903","DOI":"10.1109\/ICSES52305.2021.9633903"},{"key":"e_1_2_10_13_1","doi-asserted-by":"publisher","DOI":"10.1155\/2022\/2858845"},{"key":"e_1_2_10_14_1","doi-asserted-by":"publisher","DOI":"10.1155\/2022\/3081748"},{"key":"e_1_2_10_15_1","doi-asserted-by":"publisher","DOI":"10.1155\/2022\/1128217"},{"key":"e_1_2_10_16_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.measen.2022.100412"},{"key":"e_1_2_10_17_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.mlwa.2021.100044"},{"issue":"3","key":"e_1_2_10_18_1","doi-asserted-by":"crossref","first-page":"5783","DOI":"10.53730\/ijhs.v6nS3.7233","article-title":"Brain tumor detection and classification by MRI images using deep learning techniques","volume":"6","author":"Mangla R","year":"2022","journal-title":"Int J Health Sci"},{"key":"e_1_2_10_19_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jksuci.2020.08.006"},{"key":"e_1_2_10_20_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.procs.2022.03.063"},{"key":"e_1_2_10_21_1","doi-asserted-by":"publisher","DOI":"10.1155\/2022\/1465173"},{"key":"e_1_2_10_22_1","doi-asserted-by":"publisher","DOI":"10.1155\/2021\/2392395"},{"key":"e_1_2_10_23_1","doi-asserted-by":"publisher","DOI":"10.1155\/2022\/2761847"},{"key":"e_1_2_10_24_1","doi-asserted-by":"publisher","DOI":"10.1155\/2022\/2980691"},{"key":"e_1_2_10_25_1","first-page":"1","article-title":"Automatic segmentation of MRI of brain tumor using deep convolutional network","volume":"2022","author":"Zhou R","year":"2022","journal-title":"Biomed Res Int"},{"key":"e_1_2_10_26_1","doi-asserted-by":"publisher","DOI":"10.1155\/2022\/5465279"},{"key":"e_1_2_10_27_1","doi-asserted-by":"publisher","DOI":"10.1155\/2021\/5513500"},{"key":"e_1_2_10_28_1","doi-asserted-by":"publisher","DOI":"10.3390\/diagnostics12041018"},{"key":"e_1_2_10_29_1","doi-asserted-by":"publisher","DOI":"10.1155\/2022\/3264367"},{"key":"e_1_2_10_30_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10548-023-00953-0"},{"key":"e_1_2_10_31_1","doi-asserted-by":"publisher","DOI":"10.3390\/math10030384"},{"key":"e_1_2_10_32_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-020-05429-x"},{"key":"e_1_2_10_33_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2021.118409"},{"key":"e_1_2_10_34_1","doi-asserted-by":"publisher","DOI":"10.3934\/mbe.2021208"},{"key":"e_1_2_10_35_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.techfore.2021.120762"},{"key":"e_1_2_10_36_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10278-021-00455-0"},{"key":"e_1_2_10_37_1","unstructured":"AETiC >> Archive.2023.http:\/\/aetic.theiaer.org\/archive\/v5\/v5n2\/p7.html"},{"key":"e_1_2_10_38_1","doi-asserted-by":"crossref","unstructured":"ZhangD WangJ ZhaoX.Estimating the Uncertainty of Average F1 Scores: ICTIR '15: Proceedings of the 2015 International Conference on The Theory of Information Retrieval Northampton Massachusetts 27\u201030 September; 2015:317\u2010320. doi:10.1145\/2808194.2809488","DOI":"10.1145\/2808194.2809488"},{"key":"e_1_2_10_39_1","doi-asserted-by":"publisher","DOI":"10.1111\/exsy.12611"},{"key":"e_1_2_10_40_1","doi-asserted-by":"publisher","DOI":"10.5858\/arpa.2020-0716-SA"},{"key":"e_1_2_10_41_1","doi-asserted-by":"publisher","DOI":"10.1145\/3592615"}],"container-title":["International Journal of Imaging Systems and Technology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/ima.22949","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,26]],"date-time":"2024-10-26T14:13:16Z","timestamp":1729951996000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1002\/ima.22949"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,8,18]]},"references-count":40,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2024,1]]}},"alternative-id":["10.1002\/ima.22949"],"URL":"https:\/\/doi.org\/10.1002\/ima.22949","archive":["Portico"],"relation":{},"ISSN":["0899-9457","1098-1098"],"issn-type":[{"value":"0899-9457","type":"print"},{"value":"1098-1098","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,8,18]]},"assertion":[{"value":"2023-04-20","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2023-07-29","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2023-08-18","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"e22949"}}