Breast cancer diagnosis from ultrasound imaging poses a significant challenge due to the difficulty of simultaneously distinguishing between normal, benign, and malignant tissues within a unified framework. Existing approaches often prioritize binary classification, overlooking the clinical necessity for comprehensive multi-class differentiation. In this work, we propose an end-to-end convolutional neural network (CNN) architecture that directly addresses this gap. Leveraging a modified ResNet-18 backbone with domain-specific adaptations, our model integrates feature extraction and multi-class decision-making into a single efficient pipeline. Trained on the BUSI dataset, the proposed framework achieves 92.31% accuracy in multi-class classification, outperforming state-of-the-art methods by a significant margin. Through extensive ablation studies, we demonstrate the robustness and scalability of our approach, highlighting its clinical relevance for early detection and effective management of breast cancer. This work sets a new benchmark for ultrasound-based breast cancer diagnostics, offering a reliable and interpretable framework for real-world deployment.

In our research paper, we introduce a distinctive Bengali OCR system that boasts impressive capabilities. This system excels in reconstructing document layouts while maintaining the integrity of structure, alignment, and even images. It integrates advanced image and signature detection for precise extraction. Specifically, tailored models for word segmentation accommodate various document types, such as computer-compose, letterpress, typewritten, and handwritten documents. Notably, the system handles static and dynamic handwritten inputs, recognizing diverse writing styles. Additionally, it achieves remarkable recognition of compound characters in the Bengali language. The comprehensive data collection contributes to a diverse corpus, and sophisticated technical components enhance character and word recognition. Other notable features include image, logo, signature recognition, table recognition, perspective correction, layout reconstruction, and a queuing module for efficient and scalable processing. The system showcases exceptional performance in the efficient and accurate extraction and analysis of text.

Bangla is among the ten most popular languages in the world by the number of speakers. The task of Bangla recognition is quite challenging than other languages because of the existence of graphemes of multiple single characters, and diacritics of vowels and consonants. The purpose of this study is to develop an innovative large-scale Bangla OCR solution based on character-level recognition. Two types of documents were used to test our method: handwritten and printed. In addition, our method was applied to the handwritten documents as well as three subdomains of the printed domain: computer-composed, letterpress, and typewritten documents using our proposed attentionbased multi-headed CNN architecture. Extensive testing shows that our method provides state-of-the-art performance on both handwritten and printed texts.

CoViD-19 pandemic has shown that we have deep gaps in understanding this extremely infectious virus—not only both from a clinical diagnosis and treatment perspective—but also from a forecasting point of view, so that we are better prepared for the next onset of a similar pandemic, which, at this point, seems almost inevitable. In this paper, we present a novel approach towards modeling influenza, a closely related disease to CoViD-19, marrying clinical understanding with artificial intelligence, exploiting the Forest Deep Neural Network (fDNN) with accuracy rates in the 90% range.

Bangla is one of the world's most widely-spoken languages, but few languages (or "script") automation solutions have been reported for it. To build an OCR system, it is very important to detect the language and type of printing style to run specific character recognition and segmentation modules. This paper presents a novel solution to automatically detect the language (Bangla vs English in terms of the script), and printing style (printed vs handwritten) from any given bilingual scanned document using multiple deep learning models.

The rise of artificial intelligence technology along with machine and deep learning are opening up almost limitless possibilities. In recent years, application-based researchers in machine learning and deep learning have started developing solutions for many practical problems. Handwriting recognition is one such area of interest. Bangla, being the seventh most spoken language in the world, is not an exception. However, unlike English, there have not been concerted formal attempts in building a benchmark in comparing the different approaches reported in the literature, mainly because of the lack of openly and freely available datasets and diversity of the approaches without formal comparative studies. In this research paper, we seek to rectify this gap. We have focused on benchmarking five robust algorithms: K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Random Forest (RF), Multi-layer Perceptron (MLP), Convolutional Neural Network (CNN), on all publicly available Bangla handwriting digits datasets, including Ekush, NumtaDB, CMARTdb, and BDRW. NumtaDB itself is a collection of five handwriting datasets. We have worked on fine-tuning these algorithms by finding the best possible hyper-parameters of these algorithms. It is our hope that Sankhya will work as a beginning point of an open and verifiable benchmarking process that we plan to repeat every two years for now on and set a standard for testing and validating newer and novel algorithms that will be reported in this area in the future. In addition, we have extensively compared our research with other states of the art research and our versions of these algorithms are now outperforming every reported result on these datasets.All the datasets we used are open-sourced. In addition, we are making the.csv version of these datasets available in public GitHub. Of all the models we tested, the Sankhya CNN model performed the best for all these datasets, which we fine-tuned specifically for Bangla character recognition. We are making this CNN model available in public GitHub.