Rapid and precise identification of Coronavirus Disease 2019 (COVID-19) is pivotal for effective patient care, comprehending the pandemic’s trajectory, and enhancing long-term patient survival rates. Despite numerous recent endeavors in medical imaging, many convolutional neural network-based models grapple with the expressiveness problem and overfitting, and the training process of these models is always resource-intensive. This paper presents an innovative approach employing Xception, augmented with cutting-edge transfer learning techniques to forecast COVID-19 from X-ray thorax images. Our experimental findings demonstrate that the proposed model surpasses the predictive accuracy of established models in the domain, including Xception, VGG-16, and ResNet. This research marks a significant stride toward enhancing COVID-19 detection through a sophisticated and high-performing imaging model.

Underrepresented languages such as Lingala are a significant part of the world’s cultural and linguistic heritage. Lingala plays a central role in daily communication, business, media, education, and culture for millions of people in the Democratic Republic of Congo (DRC) and the Republic of Congo. However, due to data scarcity and dialectal diversity, natural language processing (NLP) research often overlooks this language. In this paper, we propose a deep neural network pipeline for bidirectional French–Lingala automatic translation, covering both text-to-text and voice-to-text scenarios, by integrating Long Short-Term Memory (LSTM) and Transformer models on a specialized parallel corpus. The Bidirectional Encoder Representations from Transformers (BERT) model is used as a bidirectional source encoder to improve contextual representation, while the Whisper model handles automatic speech recognition as the first stage of the audio translation pipeline. Experimental results show that the standalone Transformer achieves a BLEU score of 35.3, compared to 8.12 for the LSTM SeqToSeq baseline. Fine-tuning with BERT raises the BLEU score to 38.6. Integrating the Whisper ASR module for an end-to-end speech translation task yields a final pipeline BLEU score of 55.4, with a Word Error Rate of 12.3% on the speech recognition sub-task, confirming the effectiveness of each component. These results demonstrate the potential of combining domain-specific pre-trained models with modular neural architectures to achieve competitive translation performance in a critically under-resourced language.