In this review, we compiled convolutional neural network (CNN) methods which have the potential to automate the manual, costly and error-prone processing of medical images. We attempted to provide a thorough survey of improved architectures, popular frameworks, activation functions, ensemble techniques, hyperparameter optimizations, performance metrics, relevant datasets and data preprocessing strategies that can be used to design robust CNN models. We also used machine learning algorithms for the statistical modeling of the current literature to uncover latent topics, method gaps, prevalent themes and potential future advancements. The statistical modeling results indicate a temporal shift in favor of improved CNN designs, such as a shift from the use of a CNN architecture to a CNN-transformer hybrid. The insights from statistical modeling point that the surge of CNN practitioners into the medical imaging field, partly driven by the COVID-19 challenge, catalyzed the use of CNN methods for detecting and diagnosing pathological conditions. This phenomenon likely contributed to the sharp increase in the number of publications on the use of CNNs for medical imaging, both during and after the pandemic. Overall, the existing literature has certain gaps in scope with respect to the design and optimization of CNN architectures and methods specifically for medical imaging. Additionally, there is a lack of post hoc explainability of CNN models and slow progress in adopting CNNs for low-resource medical imaging. This review ends with a list of open research questions that have been identified through statistical modeling and recommendations that can potentially help set up more robust, improved and reproducible CNN experiments for medical imaging.

Introduction: Early diagnosis of cervical cancer at the precancerous stage is critical for effective treatment and improved patient outcomes. Objective: This study aims to explore the use of SWIN Transformer and Convolutional Neural Network (CNN) hybrid models combined with transfer learning to classify precancerous colposcopy images. Methods: Out of 913 images from 200 cases obtained from the Colposcopy Image Bank of the International Agency for Research on Cancer, 898 met quality standards and were classified as normal, precancerous, or cancerous based on colposcopy and histopathological findings. The cases corresponding to the 360 precancerous images, along with an equal number of normal cases, were divided into a 70/30 train–test split. The SWIN Transformer and CNN hybrid model combines the advantages of local feature extraction by CNNs with the global context modeling by SWIN Transformers, resulting in superior classification performance and a more automated process. The hybrid model approach involves enhancing image quality through preprocessing, extracting local features with CNNs, capturing the global context with the SWIN Transformer, integrating these features for classification, and refining the training process by tuning hyperparameters. Results: The trained model achieved the following classification performances on fivefold cross-validation data: a 94% Area Under the Curve (AUC), an 88% F1 score, and 87% accuracy. On two completely independent test sets, which were never seen by the model during training, the model achieved an 80% AUC, a 75% F1 score, and 75% accuracy on the first test set (precancerous vs. normal) and an 82% AUC, a 78% F1 score, and 75% accuracy on the second test set (cancer vs. normal). Conclusions: These high-performance metrics demonstrate the models’ effectiveness in distinguishing precancerous from normal colposcopy images, even with modest datasets, limited data augmentation, and the smaller effect size of precancerous images compared to malignant lesions. The findings suggest that these techniques can significantly aid in the early detection of cervical cancer at the precancerous stage.

Cervical cancer represents a significant global health concern, stemming from persistent infections with high-risk types of human papillomavirus (HPV). The understanding of cervical cancer’s clinical correlates, risk factors, molecular mechanisms, stages, and associated genetic mutations is important for early detection and improved treatment strategies. Through integrated analysis of clinical and molecular datasets, this study aims to identify key factors that are overlapping and distinct across four cohorts of different races and regions. Here, datasets from four distinct cohorts of patients from Uganda (N = 212), the United States of America (USA) (N = 228), China (N = 106), and Venezuela (N = 858) were examined to comprehensively explore the relationships between cervical cancer stages, HPV types (clades), productive HPV integration, and malignant genetic mutations. Cohort-specific findings included the occurrence frequencies of cervical cancer stages and grades. The majority of patients from the USA and China were diagnosed with stages I and II, while those from Uganda were diagnosed with stages II and III, reflecting levels of awareness and the availability of HPV vaccines and screening services. Conversely, cervical cancer and its stages were positively correlated with HPV types (clades), HPV integration, and risk-factor habits across the cohorts. Our findings indicate that the more common squamous cervical cancer can be potentially due to productive HPV16 (clade 9) integration. At the molecular level, pathways related to HPV infection, cancer-related conditions, and viral carcinogenesis were among the most significant pathways associated with mutated genes in cervical cancer (across cohorts). These findings collectively corroborate the prominent role of HPV infection and integration leading to genetic mutation and hence to the development of cervical cancer and its stages across patients of distinct races and regions.

Aspect-based sentiment analysis (ABSA) is the subfield of natural language processing that deals with essentially splitting data into aspects and finally extracting the sentiment polarity as positive, negative, or neutral. ABSA has been widely investigated and developed for many resource-rich languages such as English and French. However, little work has been done on indigenous African languages like Afaan Oromoo both at the document and sentence levels. In this paper, ABSA for Afaan Oromoo movie reviews was investigated and developed. To achieve the proposed objective, 2800 Afaan Oromoo movie reviews were collected from YouTube using YouTube Data API. Following the data preprocessing, predetermined aspects of the Afaan Oromoo movie were extracted and labeled into positive or negative aspects by domain experts. For implementation, different machine learning algorithms including random forest, logistic regression, SVM, and multinomial naïve Bayes in combination with BoW and TF-IDF were applied. To test and measure the proposed system, accuracy, precision, recall, and f1-score were used. In the case of random forest, the accuracy obtained in combination with both BoW and TF-IDF was 88. Using the SVM, the accuracy generated with BoW and TF-IDF was 88 and 87, respectively. Applying logistic regression, the accuracy generated with both BoW and TF-IDF was 87. Using multinomial naïve Bayes, the accuracy generated in combination with both BoW and TF-IDF was 88. To improve the optimal performance evaluation parameters, different hyperparameter tuning settings were applied. The implementation result shows that the optimal values of models performance evaluation parameters were generated using different hyperparameter tuning settings.

Nowadays, social media platforms provide space that allows communication and sharing of various resources using a variety of natural languages in different cultural and multilingual aspects. Although this interconnectedness offers numerous benefits, it also exposes users to the risk of encountering offensive (OFFN) and harmful content, including hateful speech. In order to create a model for detecting hateful content in resource‐rich languages, lexicons, word embedding, topic modeling, and transformer language models were applied. Low‐resource languages, including Ethiopian languages, suffering in lack of such linguistic resources. Multilingual hateful content detection brings complex challenges due to cultural and linguistic varieties. The paper proposes a multilingual hateful content identification model using a transformer language model and hybrid lexicon techniques to enhance hateful content recognition in low‐resource Ethiopian languages. First, hateful content disseminated on Facebook in Ethiopian‐languages target was identified as (insult, identity hate, antagonistic, and threat) using topic modeling techniques. Then, we compiled different hateful terms from sources such as guidelines and proclamations related to the Ethiopian context. We created Ethiopian context‐based transformer language models. We utilized topic words‐based datasets to construct pretrained transformer language models and multilingual lexicons of major Ethiopian languages. Finally, their performance was compared by integrating them into deep learning‐based low‐resource Ethiopian languages’ hateful content detection framework. Among applied deep learning algorithms with Ethiopian language linguistic resources, word2vec‐based multilingual lexicons with convolutional neural network (CNN) outperform than others. The result indicated that constructing topic words based multilingual word2vec lexicons outperformed than transformers language model based on topics modeling for low‐resource Ethiopian languages, effectively produce the promising hate speech (HATE) detection approach of low‐resource Ethiopian languages.

Human cervical cancer and pre-cancer research relies on datasets scattered across modality-specific archives, imaging repositories, benchmark platforms, trial registries, and controlled-access catalogs. This fragmentation—combined with heterogeneous metadata, ambiguous use of “cervical” terminology, and inconsistent indexing of pre-cancer and screening/triage resources—limits reproducible discovery, access planning, and cross-modal benchmarking. We present the Cervical Cancer Dataset Catalog (CCDCAT), a machine-readable, versioned dataset of datasets that enumerates host-specific dataset-instance records anchored to stable identifiers and resolvable landing records within an explicitly declared discoverable source universe (U_v1.0) and a frozen discovery/labeling lexicon (Q_v1.0). The CCDCAT spans invasive cervical cancer, pre-cancer/dysplasia, and cervix-focused screening and triage phenotypes, and it covers molecular omics, imaging and microscopy (including cervix photography, cytology, and digital pathology), trial registry records, benchmark resources, and controlled-access catalogs represented as metadata with explicit access pathways. Eligibility and labels are assigned conservatively from source-provided metadata; when evidence is insufficient, the CCDCAT abstains rather than infers. In the initial release (CCDCAT-U_v1.0; v0.1), we enumerate 14 eligible dataset instances across 11 host systems within a declared universe of 21 sources. Releases include manuscript-ready tables and interoperable artifacts (schema, controlled vocabularies, provenance logs, abstention ledgers, and a queryable database), enabling reproducible filtering, linkage, and auditable reuse planning.