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Letteratura scientifica selezionata sul tema "Automated Cancer Diagnosis"

Autore: Grafiati

Pubblicato: 4 giugno 2025

Ultima modifica: 1 agosto 2025

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Articoli di riviste sul tema "Automated Cancer Diagnosis"

1

Singh, Seema, V. Tejaswini, Rishya P. Murthy, and Amit Mutgi. "Neural Network Based Automated System for Diagnosis of Cervical Cancer." International Journal of Biomedical and Clinical Engineering 4, no. 2 (2015): 26–39. http://dx.doi.org/10.4018/ijbce.2015070103.

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Cervical Cancer is one of the most common cancers among women worldwide. Few concerns have arisen such as the shortage of skilled pathologists leading to increase in burden on them. This requires a need for efficient and accurate method that diagnoses cervical cancer without human intervention. In this paper, an automated system is developed for diagnosis of cervical cancer using image processing techniques and neural networks. The system is developed using Cytology images taken from Bangalore based cancer pathologist. MATLAB image processing toolbox is used to extract features from cytology i

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N., Gangatharan, Muthumanickam S., Rajagopal R., Sudharsanam V., Sai Rakesh K. V., and Sanjive Kumaran B. "Enhancing Skin Cancer Diagnosis with Deep Learning-Based Classification." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 5s (2023): 105–11. http://dx.doi.org/10.17762/ijritcc.v11i5s.6634.

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The diagnosis of skin cancer has been identified as a significant medical challenge in the 21st century due to its complexity, cost, and subjective interpretation. Early diagnosis is critical, especially in fatal cases like melanoma, as it affects the likelihood of successful treatment. Therefore, there is a need for automated methods in early diagnosis, especially with a diverse range of image samples with varying diagnoses. An automated system for dermatological disease recognition through image analysis has been proposed and compared to conventional medical personnel-based detection. This p

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Gurumoorthy, R., and M. Kamarasan. "Automated Breast Cancer Diagnosis using Optimization Algorithm with Deep Learning on Histopathological Images." International Journal of Science and Research (IJSR) 12, no. 12 (2023): 1506–12. http://dx.doi.org/10.21275/sr231218172554.

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Demir, C., S. H. Gultekin, and B. Yener. "Augmented cell-graphs for automated cancer diagnosis." Bioinformatics 21, Suppl 2 (2005): ii7—ii12. http://dx.doi.org/10.1093/bioinformatics/bti1100.

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Min, Jouha, Lip Ket Chin, Juhyun Oh, et al. "CytoPAN—Portable cellular analyses for rapid point-of-care cancer diagnosis." Science Translational Medicine 12, no. 555 (2020): eaaz9746. http://dx.doi.org/10.1126/scitranslmed.aaz9746.

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Rapid, automated, point-of-care cellular diagnosis of cancer remains difficult in remote settings due to lack of specialists and medical infrastructure. To address the need for same-day diagnosis, we developed an automated image cytometry system (CytoPAN) that allows rapid breast cancer diagnosis of scant cellular specimens obtained by fine needle aspiration (FNA) of palpable mass lesions. The system is devoid of moving parts for stable operations, harnesses optimized antibody kits for multiplexed analysis, and offers a user-friendly interface with automated analysis for rapid diagnoses. Throu

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Alharbi, Abir. "An Automated Computer System Based on Genetic Algorithm and Fuzzy Systems for Lung Cancer Diagnosis." International Journal of Nonlinear Sciences and Numerical Simulation 19, no. 6 (2018): 583–94. http://dx.doi.org/10.1515/ijnsns-2017-0048.

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AbstractAn automated system for the diagnosis of lung cancer is proposed in this paper, the system is designed by combining two major methodologies, namely the fuzzy base systems and the evolutionary genetic algorithms (GAs), to be employed on lung cancer data to assist physicians in the early detection of lung cancers, and hence obtain an early automated diagnosis complementary to that by physicians. Our hybrid algorithm, the genetic-fuzzy algorithm, has produced optimized diagnosis systems that attain high classification performance, in fact, our best six rule system obtained a 97.5 % accura

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Altunbay, D., C. Cigir, C. Sokmensuer, and C. Gunduz-Demir. "Color Graphs for Automated Cancer Diagnosis and Grading." IEEE Transactions on Biomedical Engineering 57, no. 3 (2010): 665–74. http://dx.doi.org/10.1109/tbme.2009.2033804.

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Raphael, A. P., and H. P. Soyer. "Automated diagnosis: shedding the light on skin cancer." British Journal of Dermatology 178, no. 2 (2018): 331–33. http://dx.doi.org/10.1111/bjd.16219.

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Beaulieu, Robert J., Seth D. Goldstein, Jasvinder Singh, Bashar Safar, Amit Banerjee, and Nita Ahuja. "Automated diagnosis of colon cancer using hyperspectral sensing." International Journal of Medical Robotics and Computer Assisted Surgery 14, no. 3 (2018): e1897. http://dx.doi.org/10.1002/rcs.1897.

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Dr., Monalisa Hati and Safa Asif Dabir. "Implementing Deep Learning in Automated Blood Cancer Diagnosis." Academic 2, no. 10 (2024): 770–79. https://doi.org/10.5281/zenodo.14106657.

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Tesi sul tema "Automated Cancer Diagnosis"

1

Denton, William Edward. "Boundary and shape recognition for automated skin tumour diagnosis." Thesis, Bangor University, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.263973.

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Leong, F. Joel W.-M. "An automated diagnostic system for tubular carcinoma of the breast : a model for computer-based cancer diagnosis." Thesis, University of Oxford, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.249491.

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Ho, Wing-lun, and 何穎麟. "Application of an automated DNA-imager in cervical cancer screening." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2014. http://hdl.handle.net/10722/206506.

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Abstract (sommario):

In cervical screening programmes, Papanicolaou test (Pap test) is the key screening tool. However Pap test is difficult to implement in low-resource region. Introduction of an economic, cost-effective and less skill demanding equipment is hence a potential direction of advance in cervical screening methodology. 　　Cervical carcinogenesis involves genetic instability which leads to chromosomal aneuploidy. Evaluation of aneuploidy may hence provide information for identifying cancer and precursor cells. An automated DNA-image-cytometry system (DNA-imager) capable of quantitating the DNA c

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Wortman, Tyler David. "LesionAir : a low-cost tool for automated skin cancer diagnosis and mapping." Thesis, Massachusetts Institute of Technology, 2016. http://hdl.handle.net/1721.1/104499.

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Thesis: Ph. D., Massachusetts Institute of Technology, Department of Mechanical Engineering, 2016.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 235-252).<br>Skin cancer is the most common form of cancer in the United States; one out of every five Americans develops skin cancer at some point in their lifetime. Diagnosing cancerous lesions early is critical as it significantly increases the chance of survival. However, current techniques for diagnosing skin cancer lack specificity and sensitivity, resulting in many unnecessary biopsies and missed diagnos

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Sarfati, Emma. "Exploiting Heterogeneous Labels in Deep Learning for Medical Image Analysis : Application to the Automated Diagnosis of Liver Diseases." Electronic Thesis or Diss., Institut polytechnique de Paris, 2025. http://www.theses.fr/2025IPPAT017.

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Le cancer du foie est l’un des plus meurtriers au monde, avec un ratio mortalité/incidence élevé et des conséquences sanitaires et économiques majeures. Parmi les principaux facteurs de risque figurent les infections chroniques par les virus de l’hépatite B et C, la stéatohépatite non alcoolique (NASH), l’alcool et certaines substances hépatotoxiques. La cirrhose constitue le principal facteur prédisposant au carcinome hépatocellulaire (CHC), présente dans la majorité des cas. Les diagnostics de la cirrhose et du CHC reposent le plus souvent sur la biopsie hépatique, une procédure invasive et

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Watts, K. "Quantitative DNA analysis and immunocytochemical studies : applications in the diagnosis of cervical neoplasia and automated cervical cancer screening." Thesis, Imperial College London, 1988. http://hdl.handle.net/10044/1/47297.

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Neethling, Greta Sophie. "Automated sputum screening using the BD FocalPointTM Slide Profiler : correlation with transbronchial and transthoracic needle aspirates in a high risk population." Thesis, Stellenbosch : Stellenbosch University, 2014. http://hdl.handle.net/10019.1/86677.

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Thesis (MMed)--Stellenbosch University, 2014.<br>ENGLISH ABSTRACT: Background: Sputum is a non-invasive, economic investigation whereby bronchogenic carcinoma can be identified. Manual cytological screening is labour intensive, time-consuming and requires a continuous high level of alertness. Automation has recently been successfully introduced in gynaecological cytology. Since sputum samples are similar to cervical smears, the question arises as to whether they are also suitable for automated screening. Objective: This study presented with various objectives: 1) To test automated sput

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Paoli, Marine de. "Cancer de la vessie : sélection de biomarqueurs urinaires et développement d’un outil d’analyse multiparamétrique pour le diagnostic et la récidive des tumeurs urothéliales." Thesis, Lyon, 2016. http://www.theses.fr/2016LYSE1158/document.

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Les travaux présentés dans cette thèse concernent le développement d'un outil d'analyse multiparamétrique pour la quantification de biomarqueurs urinaires du cancer de la vessie.La première partie des travaux de recherche a pour objectif la sélection de marqueurs pour le diagnostic et la récidive des tumeurs urothéliales. Une première étude a permis l'évaluation de la sélectivité de marqueurs candidats dans des échantillons urinaires de patients atteints de cancer de la vessie. Cinq des vingt marqueurs initiaux ont été sélectionnés pour leur performance diagnostique, définissant le Panel 1 : V

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Round, Andrew John. "Texture and colour for automatic image-based skin lesion analysis." Thesis, Bangor University, 1998. https://research.bangor.ac.uk/portal/en/theses/texture-and-colour-for-automatic-imagebased-skin-lesion-analysis(6f44bc07-d680-4aee-941b-49ba4ddd5314).html.

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The research presented here considers automatic diagnosis support for skin cancer. The role of computer-based diagnosis, and its value within a primary care situation are examined resulting in synthesis of aims, requirements and properties for an effective system -a system based on digital optical images captured and processed using low-cost commercial computer technology. The issues involved in acquisition of lesion boundaries are discussed. The value of accurate and robust boundaries, in terms of both directly obtainable diagnostic features and in enabling lesion property evaluation, is iden

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Lu, Dingran. "Multi-circle Detections for an Automatic Medical Diagnosis System." DigitalCommons@CalPoly, 2012. https://digitalcommons.calpoly.edu/theses/735.

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Real-time multi-circle detection has been a challenging problem in the field of biomedical image processing, due to the variable sizes and non-ideal shapes of cells in microscopic images. In this study, two new multi-circle detection algorithms are developed to facilitate an automatic bladder cancer diagnosis system: one is a modified circular Hough Transform algorithm integrated with edge gradient information; and the other one is a stochastic search approach based on real valued artificial immune systems. Computer simulation results show both algorithms outperform traditional methods such as

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Libri sul tema "Automated Cancer Diagnosis"

1

K, Grohs Heinz, and Husain O. A. N, eds. Automated cervical cancer screening. Igaku-Shoin, 1994.

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A. M. J. van Driel-Kulker. Automated image analysis apllied to the diagnosis of cervical cancer. 1986.

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Capitoli di libri sul tema "Automated Cancer Diagnosis"

1

Kalkan, Habil, Marius Nap, Robert P. W. Duin, and Marco Loog. "Automated Colorectal Cancer Diagnosis for Whole-Slice Histopathology." In Medical Image Computing and Computer-Assisted Intervention – MICCAI 2012. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33454-2_68.

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2

Gupta, Surbhi. "Automated Diagnosis of Breast Cancer: An Ensemble Approach." In Advances in Data Computing, Communication and Security. Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-8403-6_18.

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Velikova, Marina, Inês Dutra, and Elizabeth S. Burnside. "Automated Diagnosis of Breast Cancer on Medical Images." In Foundations of Biomedical Knowledge Representation. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-28007-3_4.

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4

Ghani, Arfan. "Computer Vision-Based Automated Diagnosis for Skin Cancer Detection." In EAI/Springer Innovations in Communication and Computing. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-60140-8_7.

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Alam, Kamran, Lalita Sharma, and Namarta Chopra. "Automated Breast Cancer Diagnosis Based on Neural Network Algorithms." In Intelligent Systems Reference Library. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-71975-3_6.

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6

Yadav, Lowlesh N., Shradhesh R. Marve, Varsha H. Sadrani, Shreyas R. Shende, and Nita J. Gedam. "Automated diagnosis of brain tumors from MRI scans using U-Net segmentation." In Artificial Intelligence Revolutionizing Cancer Care. CRC Press, 2024. https://doi.org/10.1201/9781003571339-15.

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Mishra, Sumita, Naresh Kumar Chaudhary, Pallavi Asthana, and Anil Kumar. "Deep 3D Convolutional Neural Network for Automated Lung Cancer Diagnosis." In Lecture Notes in Networks and Systems. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-7150-9_16.

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Sabata, Bikash, Boris Babenko, Robert Monroe, and Chukka Srinivas. "Automated Analysis of PIN-4 Stained Prostate Needle Biopsies." In Prostate Cancer Imaging. Computer-Aided Diagnosis, Prognosis, and Intervention. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-15989-3_11.

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Menzies, S. W., L. M. Bischof, G. Peden, et al. "Automated Instrumentation for the Diagnosis of Invasive Melanoma: Image Analysis of Oil Epiluminescence Microscopy." In Skin Cancer and UV Radiation. Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/978-3-642-60771-4_122.

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Yan, Ke, and Huijuan Lu. "Evaluating Ensemble Learning Impact on Gene Selection for Automated Cancer Diagnosis." In Precision Health and Medicine. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-24409-5_18.

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Atti di convegni sul tema "Automated Cancer Diagnosis"

1

Srivastava, Pratima, Rekha Devrani, and Mansi Kukreja. "Automated Diagnosis of Breast Cancer Using Deep Learning." In 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT). IEEE, 2024. http://dx.doi.org/10.1109/icccnt61001.2024.10725541.

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G, Saravanan, Jeevanantham, S. M. Prabin, and R. Parthasarathy. "Automated Breast Cancer Diagnosis Using Deep Learning CNN Models." In 2024 International Conference on Emerging Research in Computational Science (ICERCS). IEEE, 2024. https://doi.org/10.1109/icercs63125.2024.10895656.

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Heriz, Oumaima. "Automated Machine Learning for Breast Cancer Diagnosis Using TPOT." In 2025 International Symposium on iNnovative Informatics of Biskra (ISNIB). IEEE, 2025. https://doi.org/10.1109/isnib64820.2025.10983256.

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Sundar, Koushik, Vijay K, Krissh Shankaran N, and Eashaan Manohar. "Automated Polyp Detection in Colorectal Cancer Diagnosis using Deep Learning Techniques." In 2024 8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC). IEEE, 2024. http://dx.doi.org/10.1109/i-smac61858.2024.10714685.

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Arun, Lathika, and Smitha John. "Automated Esophageal Cancer Detection using Convolutional Neural Networks for Early Diagnosis." In 2025 Fourth International Conference on Smart Technologies, Communication and Robotics (STCR). IEEE, 2025. https://doi.org/10.1109/stcr62650.2025.11019782.

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V, Raji, Anburaman Seetharaman, Baskar Kasi, Sakthi Govindaraju, and Paarkavy B. "Advancing Early Detection: CNN for Automated Blood Cancer Diagnosis and Anomaly Detection." In 2024 International Conference on IoT, Communication and Automation Technology (ICICAT). IEEE, 2024. https://doi.org/10.1109/icicat62666.2024.10923206.

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Agarwal, Rohit, B. Gayathri, Cherukupalli Mallikarjuna Rao, Zaid Alsalami, Monisha Jothi R, and K. Jaya Deepthi. "Automated Lung Cancer Diagnosis Using Vision Transformers (ViT) on CT Scan Data." In 2024 International Conference on IoT, Communication and Automation Technology (ICICAT). IEEE, 2024. https://doi.org/10.1109/icicat62666.2024.10923455.

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Kurniawan, Hendra, Afrig Aminuddin, Tonny Hidayat, Norhikmah Norhikmah, Kardilah Rohmat Hidayat, and Niken Larasati. "A Comparative Performance Analysis of SVM Kernels in Automated Breast Cancer Diagnosis." In 2024 International Conference on Information Technology Systems and Innovation (ICITSI). IEEE, 2024. https://doi.org/10.1109/icitsi65188.2024.10929383.

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Rajesh Kumar, S. V., and Y. Sukhi. "A Water Wheel Optimized Gated Attention Network (WOGAN) Model for an Automated Cervical Cancer Diagnosis." In 2024 International Conference on Electronic Systems and Intelligent Computing (ICESIC). IEEE, 2024. https://doi.org/10.1109/icesic61777.2024.10846088.

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Poornima, I. Gethzi Ahila, Abdul Wasim Abdul Habib Sheikh, Aruna Thota, G. Devi, Neha Pandey, and Sudipta Roy. "Automated Mitotic Nuclei Classification Using Fruitfly Optimization Algorithm with Machine Learning on Breast Cancer Diagnosis." In 2024 Second International Conference on Advances in Information Technology (ICAIT). IEEE, 2024. http://dx.doi.org/10.1109/icait61638.2024.10690651.

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Rapporti di organizzazioni sul tema "Automated Cancer Diagnosis"

1

Lo, Joseph Y. Computer-Aided Mammography Using Automated Feature Extraction for the Detection and Diagnosis of Breast Cancer. Defense Technical Information Center, 1995. http://dx.doi.org/10.21236/ada302530.

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Lo, Joseph Y. Computer-Aided Mammography Using Automated Feature Extraction for the Detection and Diagnosis of Breast Cancer,. Defense Technical Information Center, 1997. http://dx.doi.org/10.21236/ada341604.

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