Automated detection of anomalies in cervix cells using image analysis and machine learning

Moscon, Leonardo Moreira; Macedo, Nayana Damiani; Nunes, Célio Siman Mafra; Boasquevisque, Paulo César Ribeiro; Andrade, Tadeu Uggere de; Endringer, Denise Coutinho; Lenz, Dominik

doi:10.1007/s00580-018-2812-4

Cited by 3 publications

(2 citation statements)

References 22 publications

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“…Computer-assisted diagnosis is believed to ease this situation because it can potentially lower the misdiagnosis rate and also reduce the workload of cytologists [ 100 ]. Therefore, several studies have addressed the subject of automatic cervical cancer diagnosis [ 64 , 65 , 66 , 67 , 68 , 74 , 75 , 80 , 101 , 102 , 103 , 104 , 105 , 106 , 107 , 108 , 109 , 110 , 111 , 112 , 113 , 114 , 115 , 116 , 117 , 118 , 119 , 120 , 121 , 122 , 123 , 124 , 125 , 126 , 127 , 128 , 129 , 130 , 131 , 132 , 133 , 134 , 135 , 136 ]. The investigations showed that AI-assisted methods were promising, and achieved a high sensitivity and specificity in clinical cervical cytological screening [ 66 ,…”

Section: Resultsmentioning

confidence: 99%

Diagnosis of Cervical Cancer and Pre-Cancerous Lesions by Artificial Intelligence: A Systematic Review

et al. 2022

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Objective: The likelihood of timely treatment for cervical cancer increases with timely detection of abnormal cervical cells. Automated methods of detecting abnormal cervical cells were established because manual identification requires skilled pathologists and is time consuming and prone to error. The purpose of this systematic review is to evaluate the diagnostic performance of artificial intelligence (AI) technologies for the prediction, screening, and diagnosis of cervical cancer and pre-cancerous lesions. Materials and Methods: Comprehensive searches were performed on three databases: Medline, Web of Science Core Collection (Indexes = SCI-EXPANDED, SSCI, A & HCI Timespan) and Scopus to find papers published until July 2022. Articles that applied any AI technique for the prediction, screening, and diagnosis of cervical cancer were included in the review. No time restriction was applied. Articles were searched, screened, incorporated, and analyzed in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-analyses guidelines. Results: The primary search yielded 2538 articles. After screening and evaluation of eligibility, 117 studies were incorporated in the review. AI techniques were found to play a significant role in screening systems for pre-cancerous and cancerous cervical lesions. The accuracy of the algorithms in predicting cervical cancer varied from 70% to 100%. AI techniques make a distinction between cancerous and normal Pap smears with 80–100% accuracy. AI is expected to serve as a practical tool for doctors in making accurate clinical diagnoses. The reported sensitivity and specificity of AI in colposcopy for the detection of CIN2+ were 71.9–98.22% and 51.8–96.2%, respectively. Conclusion: The present review highlights the acceptable performance of AI systems in the prediction, screening, or detection of cervical cancer and pre-cancerous lesions, especially when faced with a paucity of specialized centers or medical resources. In combination with human evaluation, AI could serve as a helpful tool in the interpretation of cervical smears or images.

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Section: Resultsmentioning

confidence: 99%

Diagnosis of Cervical Cancer and Pre-Cancerous Lesions by Artificial Intelligence: A Systematic Review

et al. 2022

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“…I It was determined that 1,2,3 and 4 were the four most reliable indicators for detecting and predicting malignancy [100]. …”

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confidence: 99%

A Cervical Cancer Detection System Using Genetic Algorithm Enhanced Context-Aware Feature Selection

Iqbal,

Qureshi,

Chishti

2024

Preprint

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Cervical cancer is the fourth highest cause of death among women, but in its early stages, it shows no symptoms. A lack of effective early diagnosis is the fundamental cause of the disease's prevalence and poses the greatest obstacle to researchers. Pap smears, human papillomavirus tests, colposcopy, and biopsies are all invasive procedures that necessitate the assistance of medical professionals; they are time-consuming and do not identify cancer until a later stage. Thus, the absence of early symptoms and lack of invasive diagnostics, highlight the need for utilizing machine learning and deep learning approaches to revolutionize cervical cancer detection. To address this, we propose GCFS-CC(Genetic Algorithm based Context-aware Feature Selection for Cervical Cancer), which is a framework to detect cervical cancer from Pap smear images of cervical cells and classify these using a deep learning method. In our work, we use a genetic algorithm which is a bio-inspired algorithm, to select the best features from the acquired images. For feature selection, we incorporate the context-aware entropy gain method. We evaluated our model on one data set which combined a total of 917 normal and cancerous images, divided into seven classes. Using the selected features we build a deep learning model to validate our proposed framework. The results showed that after feature selection, the performance metrics(accuracy, precision, and recall), all improved. These results pave the way for GCFS-CC to be a valuable tool for early cervical cancer detection. This, however, comes with certain challenges, availability of high-quality images and proper parameter tuning for the genetic algorithm required for feature selection. To successfully implement machine learning-based cervical cancer detection into clinical practice, it will be necessary to address these challenges. Furthermore, any ethical and privacy concerns associated with the use of machine learning algorithms in healthcare settings must be addressed.

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An Intelligent Model of Anomaly Detection and Anomaly Localization in Images Using Hybrid Heuristic Adaptive Multiscale Attention-Based DenseNet and Cascaded Variational Autoencoder

Jenifer,

Devaki

2024

Int. J. Patt. Recogn. Artif. Intell.

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Anomaly detection and localization become necessary since humans are unable to spot visual frauds. Since it differs from the normal representation of shapes, colors, textures, size, and location, it does not have the potential to be employed in such practical applications. Hence, the automatic anomaly detection and localization framework is suggested for supporting situations like industrial systems, healthcare systems and so on. The detection and localization are the most intriguing and challenging concerns in image processing. In general, anomalies can manifest themselves in diverse ways. Detection is the process of providing information about whether an image contains modifications or not. On the other hand, localization is defined as locating the anomalies in images. Several traditional models have been deployed, but they still lack such constraints to evade performance enhancement. To sort out this issue, a novel adaptive method is recommended for anomaly detection and localization in images. Initially, the source images are acquired from standard public data sources. Once the required images are collected, such abnormal or anomalous activities are detected by adopting the new method known as Adaptive Multiscale Attention-based DenseNet (AMA-DeNet), where the hyperparameters are tuned by a hybrid algorithm known as the Hybrid Chimp Grasshopper Optimization Algorithm (HCGOA). Subsequently, the final stage is localizing the anomalies in the detected images. This is to be accomplished by using the Cascaded Variational Autoencoder (CVA), in which parameters are optimally chosen by HCGOA. After implementation, the experimental results are computed across diverse validating measures. Hence, the extensive results elucidate that the recommended model contains the potential to detect the abnormalities in the images accurately, thereby enhancing the efficiency of the new system.

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Automated detection of anomalies in cervix cells using image analysis and machine learning

Cited by 3 publications

References 22 publications

Diagnosis of Cervical Cancer and Pre-Cancerous Lesions by Artificial Intelligence: A Systematic Review

Diagnosis of Cervical Cancer and Pre-Cancerous Lesions by Artificial Intelligence: A Systematic Review

A Cervical Cancer Detection System Using Genetic Algorithm Enhanced Context-Aware Feature Selection

An Intelligent Model of Anomaly Detection and Anomaly Localization in Images Using Hybrid Heuristic Adaptive Multiscale Attention-Based DenseNet and Cascaded Variational Autoencoder

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