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Diseases are frequently understood as medical conditions connected with distinct indications and symptoms. According to a fairly wide categorization, diseases can also be categorized as mental disorders, deficient diseases, genetic diseases, degenerative diseases, self-inflicted diseases, infectious diseases, non-infectious diseases, social diseases, and physical diseases. Prevention of the diseases is of multiple instances. Primary prevention seeks to prevent illness or harm before it ever happens. Secondary prevention tries to lessen the effect of an illness or damage that has already happened. This is done through diagnosing and treating illness or injury as soon as feasible to stop or delay its course, supporting personal ways to avoid recurrence or reinjury, and implementing programs to restore individuals to their previous health and function to prevent long-term difficulties. Tertiary prevention tries to lessen the impact of a continuing sickness or injury that has enduring repercussions. Diagnosis of the disease at an earlier stage is important for the treatment of the disease. Hence, in this study, deep learning algorithms, such as VGG16, EfficientNetB4, and ResNet, are utilized to diagnose various diseases, such as Alzheimer's, brain tumors, skin diseases, and lung diseases. Chest X-rays, MRI scans, CT scans, and skin lesions are used to diagnose the mentioned diseases. Transfer learning algorithms, such as VGG16, VGG19, ResNet, InceptionV3, and EfficientNetB4, are utilized to categorize various diseases. EfficientNetB4 with the learning rate annealing, having obtained an accuracy of 94.04% on the test dataset, is observed. As a consequence, we observed that every network has unique particular skills on the multi-disease dataset, which includes chest X-rays, MRI scans, etc.,<\/jats:p>","DOI":"10.1007\/s00521-024-09900-x","type":"journal-article","created":{"date-parts":[[2024,8,1]],"date-time":"2024-08-01T06:02:37Z","timestamp":1722492157000},"page":"18979-18997","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["A novel method for the detection and classification of multiple diseases using transfer learning-based deep learning techniques with improved performance"],"prefix":"10.1007","volume":"36","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1069-1530","authenticated-orcid":false,"given":"Krishnamoorthy","family":"Natarajan","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9156-2054","authenticated-orcid":false,"given":"Suresh","family":"Muthusamy","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9223-0265","authenticated-orcid":false,"given":"Mizaj Shabil","family":"Sha","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8773-4626","authenticated-orcid":false,"given":"Kishor Kumar","family":"Sadasivuni","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2008-6388","authenticated-orcid":false,"given":"Sreejith","family":"Sekaran","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3878-0623","authenticated-orcid":false,"given":"Christober Asir Rajan","family":"Charles Gnanakkan","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6124-7152","authenticated-orcid":false,"given":"Ahmed","family":"A.Elngar","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,8,1]]},"reference":[{"key":"9900_CR1","doi-asserted-by":"publisher","first-page":"12407","DOI":"10.1007\/s12652-022-04331-9","volume":"14","author":"P Kaur","year":"2023","unstructured":"Kaur P, Harnal S, Gautam V et al (2023) A novel transfer deep learning method for detection and classification of plant leaf disease. 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To the best of my knowledge and belief, any actual, perceived, or potential conflicts between my duties as an employee and my private and business interests have been fully disclosed in this form following the journal's requirements.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"I have been informed of the risks and benefits involved, and all my questions have been answered satisfactorily. Furthermore, I have been assured that a research team member will also answer any future questions. 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