Handloomed fabrics recognition with deep learning.

Every nation treasures its handloom heritage, and in India, the handloom industry safeguards cultural traditions, sustains millions of artisans, and preserves ancient weaving techniques. To protect this legacy, a critical need arises to distinguish genuine handloom products, exemplified by the renowned "gamucha" from India's northeast, from counterfeit powerloom imitations. Our study's objective is to create an AI tool for effortless detection of authentic handloom items amidst a sea of fakes. Six deep learning architectures-VGG16, VGG19, ResNet50, InceptionV3, InceptionResNetV2, and DenseNet201-were trained on annotated image repositories of handloom and powerloom towels (17,484 images in total, with 14,020 for training and 3464 for validation). A novel deep learning model was also proposed. Despite respectable training accuracies, the pre-trained models exhibited lower performance on the validation dataset compared to our novel model. The proposed model outperformed pre-trained models, demonstrating superior validation accuracy, lower validation loss, computational efficiency, and adaptability to the specific classification problem. Notably, the existing models showed challenges in generalizing to unseen data and raised concerns about practical deployment due to computational expenses. This study pioneers a computer-assisted approach for automated differentiation between authentic handwoven "gamucha"s and counterfeit powerloom imitations-a groundbreaking recognition method. The methodology presented not only holds scalability potential and opportunities for accuracy improvement but also suggests broader applications across diverse fabric products.

A comparative assessment of deep object detection models for blood smear analysis.

A blood smear is a common type of blood test where blood sample is taken from a patient, smear is made from the sample followed by observation of red blood cells, white blood cells and platelets. A pathologist carefully observes the sample and manually counts the number of RBC, WBC and platelets. This entire process from creating a smear to manually counting each element is tedious and susceptible to human errors. That is why, with the advancement of deep learning, various object detection techniques have become useful for automating the process and mitigating human errors in blood smear analysis. This work presents a comparative assessment of three different object detection models namely Faster R-CNN, EfficientDet D3 and CenterNet Hourglass, and presents their respective inference results. The three models have been compared using the COCO evaluation metrics to identify the best model performance for the given task. It is observed that out of the three models, the Faster R-CNN model performs the best in detecting WBCs and platelets in microscopic blood smear images with an average precision of 99.4%. Critical tasks like medical image processing require accurate predictions to prevent unintended ramifications. Therefore, while slower in terms of inference time, Faster R-CNN is the go-to model where accuracy is the priority. The work is also compared with the existing work in this domain to prove its efficiency.

Study of morphological and textural features for classification of oral squamous cell carcinoma by traditional machine learning techniques.

BACKGROUND: Oral squamous cell carcinoma (OSCC) is the most prevalent form of oral cancer. Very few researches have been carried out for the automatic diagnosis of OSCC using artificial intelligence techniques. Though biopsy is the ultimate test for cancer diagnosis, analyzing a biopsy report is a very much challenging task. To develop computer-assisted software that will diagnose cancerous cells automatically is very important and also a major need of the hour. AIM: To identify OSCC based on morphological and textural features of hand-cropped cell nuclei by traditional machine learning methods. METHODS: In this study, a structure for semi-automated detection and classification of oral cancer from microscopic biopsy images of OSCC, using clinically significant and biologically interpretable morphological and textural features, are examined and proposed. Forty biopsy slides were used for the study from which a total of 452 hand-cropped cell nuclei has been considered for morphological and textural feature extraction and further analysis. After making a comparative analysis of commonly used methods in the segmentation technique, a combined technique is proposed. Our proposed methodology achieves the best segmentation of the nuclei. Henceforth the features extracted were fed into five classifiers, support vector machine, logistic regression, linear discriminant, k-nearest neighbors and decision tree classifier. Classifiers were also analyzed by training time. Another contribution of the study is a large indigenous cell level dataset of OSCC biopsy images. RESULTS: We achieved 99.78% accuracy applying decision tree classifier in classifying OSCC using morphological and textural features. CONCLUSION: It is found that both morphological and textural features play a very important role in OSCC diagnosis. It is hoped that this type of framework will help the clinicians/pathologists in OSCC diagnosis.

A comprehensive study on the multi-class cervical cancer diagnostic prediction on pap smear images using a fusion-based decision from ensemble deep convolutional neural network.

The diagnosis of cervical dysplasia, carcinoma in situ and confirmed carcinoma cases is more easily perceived by commercially available and current research-based decision support systems when the scenario of pathologists to patient ratio is small. The treatment modalities for such diagnosis rely exclusively on precise identification of dysplasia stages as followed by The Bethesda System. The classification based on The Bethesda System is a multiclass problem, which is highly relevant and vital. Reliance on image interpretation, when done manually, introduces inter-observer variability and makes the microscope observation tedious and time-consuming. Taking this into account, a computer-assisted screening system built on deep learning can significantly assist pathologists to screen with correct predictions at a faster rate. The current study explores six different deep convolutional neural networks- Alexnet, Vggnet (vgg-16 and vgg-19), Resnet (resnet-50 and resnet-101) and Googlenet architectures for multi-class (four-class) diagnosis of cervical pre-cancerous as well as cancer lesions and incorporates their relative assessment. The study highlights the addition of an ensemble classifier with three of the best deep learning models for yielding a high accuracy multi-class classification. All six deep models including ensemble classifier were trained and validated on a hospital-based pap smear dataset collected through both conventional and liquid-based cytology methods along with the benchmark Herlev dataset.

A shape context fully convolutional neural network for segmentation and classification of cervical nuclei in Pap smear images.

Pap smear is often employed as a screening test for diagnosing cervical pre-cancerous and cancerous lesions. Accurate identification of dysplastic changes amongst the cervical cells in a Pap smear image is thus essential for rapid diagnosis and prognosis. Manual pathological observations used in clinical practice require exhaustive analysis of thousands of cell nuclei in a whole slide image to visualize the dysplastic nuclear changes which make the process tedious and time-consuming. Automated nuclei segmentation and classification exist but are challenging to overcome issues like nuclear intra-class variability and clustered nuclei separation. To address such challenges, we put forward an application of instance segmentation and classification framework built on an Unet architecture by adding residual blocks, densely connected blocks and a fully convolutional layer as a bottleneck between encoder-decoder blocks for Pap smear images. The number of convolutional layers in the standard Unet has been replaced by densely connected blocks to ensure feature reuse-ability property while the introduction of residual blocks in the same attempts to converge the network more rapidly. The framework provides simultaneous nuclei instance segmentation and also predicts the type of nucleus class as belonging to normal and abnormal classes from the smear images. It works by assigning pixel-wise labels to individual nuclei in a whole slide image which enables identifying multiple nuclei belonging to the same or different class as individual distinct instances. Introduction of a joint loss function in the framework overcomes some trivial cell level issues on clustered nuclei separation. To increase the robustness of the overall framework, the proposed model is preceded with a stacked auto-encoder based shape representation learning model. The proposed model outperforms two state-of-the-art deep learning models Unet and Mask_RCNN with an average Zijdenbos similarity index of 97 % related to segmentation along with binary classification accuracy of 98.8 %. Experiments on hospital-based datasets using liquid-based cytology and conventional pap smear methods along with benchmark Herlev datasets proved the superiority of the proposed method than Unet and Mask_RCNN models in terms of the evaluation metrics under consideration.

Liquid based-cytology Pap smear dataset for automated multi-class diagnosis of pre-cancerous and cervical cancer lesions.

While a publicly available benchmark dataset provides a base for the development of new algorithms and comparison of results, hospital-based data collected from the real-world clinical setup is also very important in AI-based medical research for automated disease diagnosis, prediction or classifications as per standard protocol. Primary data must be constantly updated so that the developed algorithms achieve as much accuracy as possible in the regional context. This dataset would support research work related to image segmentation and final classification for a complete decision support system (https://doi.org/10.1016/j.tice.2020.101347) [1]. Liquid-based cytology (LBC) is one of the cervical screening tests. The repository consists of a total of 963 LBC images sub-divided into four sets representing the four classes: NILM, LSIL, HSIL, and SCC. It comprises pre-cancerous and cancerous lesions related to cervical cancer as per standards under The Bethesda System (TBS). The images were captured in 40x magnification using Leica ICC50 HD microscope collected with due consent from 460 patients visiting the O&G department of the public hospital with various gynaecological problems. The images were then viewed and categorized by experts of the pathology department.

Automated classification of cells into multiple classes in epithelial tissue of oral squamous cell carcinoma using transfer learning and convolutional neural network.

The analysis of tissue of a tumor in the oral cavity is essential for the pathologist to ascertain its grading. Recent studies using biopsy images reveal computer-aided diagnosis for oral sub-mucous fibrosis (OSF) carried out using machine learning algorithms, but no research has yet been outlined for multi-class grading of oral squamous cell carcinoma (OSCC). Pertinently, with the advent of deep learning in digital imaging and computational aid in the diagnosis, multi-class classification of OSCC biopsy images can help in timely and effective prognosis and multi-modal treatment protocols for oral cancer patients, thus reducing the operational workload of pathologists while enhancing management of the disease. With this motivation, this study attempts to classify OSCC into its four classes as per the Broder's system of histological grading. The study is conducted on oral biopsy images applying two methods: (i) through the application of transfer learning using pre-trained deep convolutional neural network (CNN) wherein four candidate pre-trained models, namely Alexnet, VGG-16, VGG-19 and Resnet-50, were chosen to find the most suitable model for our classification problem, and (ii) by a proposed CNN model. Although the highest classification accuracy of 92.15% is achieved by Resnet-50 model, the experimental findings highlight that the proposed CNN model outperformed the transfer learning approaches displaying accuracy of 97.5%. It can be concluded that the proposed CNN based multi-class grading method of OSCC could be used for diagnosis of patients with OSCC.

Classification of childhood medulloblastoma into WHO-defined multiple subtypes based on textural analysis.

Childhood medulloblastoma is a case of a childhood brain tumour that requires close attention due to the low survival rate. Effective prognosis depends a lot on accurate detection of its subtype. The present study proposes a texture-based computer-aided categorization of childhood medulloblastoma samples. According to the World Health Organization, it has four subtypes (desmoplastic, classic, nodular and large). Classification is done in two levels: (i) normal and abnormal and (ii) its four subtypes. The system is evaluated on indigenous patient samples collected from the region. The main objective of database generation is to create a data set of childhood medulloblastoma samples since there exists no available benchmark data set. The proposed framework for automated classification is based on the architectural property and the distribution of cells. Five texture features were extracted for the feature set, namely: grey-level co-occurrence matrix, grey-level run length matrix, first-order histogram features, local binary pattern and Tamura features. The performance of each feature set was evaluated, both individually and in combinations, using five different classifiers. Fivefold cross-validation was used for training and testing the data set. Experiments on both individual feature sets and combinations (best-2, best-3, best-4 and all-5) of feature sets were evaluated based on the accuracy of performance. It was revealed that the combined best-4 feature set resulted in the highest accuracy of 91.3%. The precision, recall and specificity were 0.913, 0.913 and 0.97, respectively. Significantly, it implied that the all-5 feature set is not necessary to have a useful classification. Feature reduction by principal component analysis resulted in increased accuracy of 96.7%. LAY DESCRIPTION: Childhood medulloblastoma is a case of childhood brain tumour that requires high attention due to a low survival rate. Effective prognosis depends a lot on accurate detection of its subtype. The present study proposes a texture-based computer-aided categorization of childhood medulloblastoma samples. According to the World Health Organization (W.H.O), it has four subtypes (desmoplastic, classic, nodular, and large). Classification is done in two levels: i) normal and abnormal ii) its four subtypes. The system is evaluated on indigenous patient samples collected from the region. The main objective of database generation is to create a data set of childhood medulloblastoma samples since there exists no available benchmark data set. The proposed framework is a model for the automatic classification of the samples. The tissue samples obtained post-operation by doctors are converted into images, and then necessary algorithms are applied so that certain features describing each group of the image are known and studied for classification. Later these images are classified using the image features into the subtypes of abnormal samples.

Automated oral squamous cell carcinoma identification using shape, texture and color features of whole image strips.

Despite profound knowledge of the incidence of oral cancers and a large body of research beyond it, it continues to beat diagnosis and treatment management. Post physical observation by clinicians, a biopsy is a gold standard for accurate detection of any abnormalities. Towards the application of artificial intelligence as an aid to diagnosis, automated cell nuclei segmentation is the most essential step for the recognition of the cancer cells. In this study, we have extracted the shape, texture and color features from the histopathological images collected indigenously from regional hospitals. A dataset of 42 whole slide slices was used to automatically segment and generate a cell level dataset of 720 nuclei. Next, different classifiers were applied for classification purposes. 99.4 % accuracy using Decision Tree Classifier, 100 % accuracy using both SVM and Logistic regression and 100 % accuracy using SVM, Logistic regression and Linear Discriminant were acquired for shape, textural and color features respectively. The in-depth analysis showed SVM and Linear Discriminant classifier gave the best result for texture and color features respectively. The achieved result can be effectively converted to software as an assistant diagnostic tool.

Histopathological imaging database for oral cancer analysis.

The repository is composed of 1224 images divided into two sets of images with two different resolutions. First set consists of 89 histopathological images with the normal epithelium of the oral cavity and 439 images of Oral Squamous Cell Carcinoma (OSCC) in 100x magnification. The second set consists of 201 images with the normal epithelium of the oral cavity and 495 histopathological images of OSCC in 400x magnification. The images were captured using a Leica ICC50 HD microscope from Hematoxyline and Eosin (H&E) stained tissue slides collected, prepared and catalogued by medical experts from 230 patients. A subset of 269 images from the second data set was used to detect OSCC based on textural features [1]. Histopathology plays a very important role in diagnosing a disease. It is the investigation of biological tissues to detect the presence of diseased cells in microscopic detail. It usually involves a biopsy. Till date biopsy is the gold-standard test to diagnose cancer. The biopsy slides are examined based on various cytological criteria under a microscope. Therefore, there is a high possibility of not retaining uniformity and ensuring reproducibility in outcomes [2, 3]. Computational diagnostic tools, on the other hand, facilitate objective judgments by making the use of the quantitative measure. This dataset can be utilized in establishing automated diagnostic tool using Artificial Intelligence approaches.

Comparative assessment of CNN architectures for classification of breast FNAC images.

Fine needle aspiration cytology (FNAC) entails using a narrow gauge (25-22 G) needle to collect a sample of a lesion for microscopic examination. It allows a minimally invasive, rapid diagnosis of tissue but does not preserve its histological architecture. FNAC is commonly used for diagnosis of breast cancer, with traditional practice being based on the subjective visual assessment of the breast cytopathology cell samples under a microscope to evaluate the state of various cytological features. Therefore, there are many challenges in maintaining consistency and reproducibility of findings. However, the advent of digital imaging and computational aid in diagnosis can improve the diagnostic accuracy and reduce the effective workload of pathologists. This paper presents a comparison of various deep convolutional neural network (CNN) based fine-tuned transfer learned classification approach for the diagnosis of the cell samples. The proposed approach has been tested using VGG16, VGG19, ResNet-50 and GoogLeNet-V3 (aka Inception V3) architectures of CNN on an image dataset of 212 images (99 benign and 113 malignant), later augmented and cleansed to 2120 images (990 benign and 1130 malignant), where the network was trained using images of 80% cell samples and tested on the rest. This paper presents a comparative assessment of the models giving a new dimension to FNAC study where GoogLeNet-V3 (fine-tuned) achieved an accuracy of 96.25% which is highly satisfactory.

A New Breast Border Extraction and Contrast Enhancement Technique with Digital Mammogram Images for Improved Detection of Breast Cancer

Purpose: Breast cancer can be cured if diagnosed early, with digital mammography which is one of the most effective imaging modalities for early detection. However mammogram images often come with low contrast, high background noises and artifacts, making diagnosis difficult. The purpose of this research is to preprocess mammogram images to improve results with a computer aided diagnosis system. The focus is on three preprocessing methods: a breast border segmentation method; a contrast enhancement method; and a pectoral muscle removal method. Methods: The proposed breast border extraction method employs a threshold based segmentation technique along with a combination of morphological operations. The contrast enhancement method presented here is divided into two phages. In phase I, a bi-level histogram modification technique is applied to enhance the image globally and in phase II a non-linear filter based on local mean and local standard deviation for each pixel is applied to the histogram modified image. The pectoral muscle removal method discussed here is implemented by applying a region growing algorithm. Results: The proposed techniques are tested with the Mini MIAS dataset. The breast border extraction method is applied to 322 images and achieved 98.7% segmentation accuracy. The contrast enhancement method is evaluated based on quantitative measures like measure of enhancement, absolute mean brightness error, combined enhancement measure and discrete entropy. The proposed contrast enhancement method when applied to 14 images with different types of masses, the quantitative measures showed an optimum level of contrast enhancement compared to other enhancement methods with preservation of local detail. Removal of the pectoral muscle from MLO mammogram images reduced the search region while identifying abnormalities like masses and calcification. Conclusions: The preprocessing steps proposed here show promising results in terms of both qualitative and quantitative analysis.

Study on Contribution of Biological Interpretable and Computer-Aided Features Towards the Classification of Childhood Medulloblastoma Cells.

Diagnosis and Prognosis of brain tumour in children is always a critical case. Medulloblastoma is that subtype of brain tumour which occurs most frequently amongst children. Post-operation, the classification of its subtype is most vital for further clinical management. In this paper a novel approach of pathological subtype classification using biological interpretable and computer-aided textural features is forwarded. The classifier for accurate features prediction is built purely on the feature set obtained by segmentation of the ground truth cells from the original histological tissue images, marked by an experienced pathologist. The work is divided into five stages: marking of ground truth, segmentation of ground truth images, feature extraction, feature reduction and finally classification. Kmeans colour segmentation is used to segment out the ground truth cells from histological images. For feature extraction we used morphological, colour and textural features of the cells followed by feature reduction using Principal Component Analysis. Finally both binary and multiclass classification is done using Support Vector Method (SVM). The classification was compared using six different classifiers and performance was evaluated employing five-fold cross-validation technique. The accuracy achieved for binary and multiclass classification before applying PCA were 95.4 and 62.1% and after applying PCA were 100 and 84.9% respectively. The run-time analysis are also shown. Results reveal that this technique of cell level classification can be successfully adopted as architectural view can be confusing. Moreover it conforms substantially to the pathologist's point of view regarding morphological and colour features, with the addition of computer assisted texture feature.

Analysis of Morphological Features of Benign and Malignant Breast Cell Extracted From FNAC Microscopic Image Using the Pearsonian System of Curves.

CONTEXT: Cytological changes in terms of shape and size of nuclei are some of the common morphometric features to study breast cancer, which can be observed by careful screening of fine needle aspiration cytology (FNAC) images. AIMS: This study attempts to categorize a collection of FNAC microscopic images into benign and malignant classes based on family of probability distribution using some morphometric features of cell nuclei. MATERIALS AND METHODS: For this study, features namely area, perimeter, eccentricity, compactness, and circularity of cell nuclei were extracted from FNAC images of both benign and malignant samples using an image processing technique. All experiments were performed on a generated FNAC image database containing 564 malignant (cancerous) and 693 benign (noncancerous) cell level images. The five-set extracted features were reduced to three-set (area, perimeter, and circularity) based on the mean statistic. Finally, the data were fitted to the generalized Pearsonian system of frequency curve, so that the resulting distribution can be used as a statistical model. Pearsonian system is a family of distributions where kappa (κ) is the selection criteria computed as functions of the first four central moments. RESULTS AND CONCLUSIONS: For the benign group, kappa (κ) corresponding to area, perimeter, and circularity was -0.00004, 0.0000, and 0.04155 and for malignant group it was 1016942, 0.01464, and -0.3213, respectively. Thus, the family of distribution related to these features for the benign and malignant group were different, and therefore, characterization of their probability curve will also be different.

Near-set Based Mucin Segmentation in Histopathology Images for Detecting Mucinous Carcinoma.

This paper introducesnear-set based segmentation method for extraction and quantification of mucin regions for detecting mucinouscarcinoma (MC which is a sub type of Invasive ductal carcinoma (IDC)). From histology point of view, the presence of mucin is one of the indicators for detection of this carcinoma. In order to detect MC, the proposed method majorly includes pre-processing by colour correction, colour transformation followed by near-set based segmentation and post-processing for delineating only mucin regions from the histological images at 40×. The segmentation step works in two phases such as Learn and Run.In pre-processing step, white balance method is used for colour correction of microscopic images (RGB format). These images are transformed into HSI (Hue, Saturation, and Intensity) colour space and H-plane is extracted in order to get better visual separation of the different histological regions (background, mucin and tissue regions). Thereafter, histogram in H-plane is optimally partitioned to find set representation for each of the regions. In Learn phase, features of typical mucin pixel and unlabeled pixels are learnt in terms of coverage of observed sets in the sample space surrounding the pixel under consideration. On the other hand, in Run phase the unlabeled pixels are clustered as mucin and non-mucin based on its indiscernibilty with ideal mucin, i.e. their feature values differ within a tolerance limit. This experiment is performed for grade-I and grade-II of MC and hence percentage of average segmentation accuracy is achieved within confidence interval of [97.36 97.70] for extracting mucin areas. In addition, computation of percentage of mucin present in a histological image is provided for understanding the alteration of such diagnostic indicator in MC detection.

Automated classification of Pap smear images to detect cervical dysplasia.

BACKGROUND AND OBJECTIVES: The present study proposes an intelligent system for automatic categorization of Pap smear images to detect cervical dysplasia, which has been an open problem ongoing for last five decades. METHODS: The classification technique is based on shape, texture and color features. It classifies the cervical dysplasia into two-level (normal and abnormal) and three-level (Negative for Intraepithelial Lesion or Malignancy, Low-grade Squamous Intraepithelial Lesion and High-grade Squamous Intraepithelial Lesion) classes reflecting the established Bethesda system of classification used for diagnosis of cancerous or precancerous lesion of cervix. The system is evaluated on two generated databases obtained from two diagnostic centers, one containing 1610 single cervical cells and the other 1320 complete smear level images. The main objective of this database generation is to categorize the images according to the Bethesda system of classification both of which require lots of training and expertise. The system is also trained and tested on the benchmark Herlev University database which is publicly available. In this contribution a new segmentation technique has also been proposed for extracting shape features. Ripplet Type I transform, Histogram first order statistics and Gray Level Co-occurrence Matrix have been used for color and texture features respectively. To improve classification results, ensemble method is used, which integrates the decision of three classifiers. Assessments are performed using 5 fold cross validation. RESULTS: Extended experiments reveal that the proposed system can successfully classify Pap smear images performing significantly better when compared with other existing methods. CONCLUSION: This type of automated cancer classifier will be of particular help in early detection of cancer.

On the Study of Pre-Pregnancy Body Mass Index (BMI) and Weight Gain as Indicators of Nutritional Status of Pregnant Women Belonging to Low Socio-Economic Category: A Study from Assam.

Women, particularly pregnant women, are the most vulnerable population of the society and their health status is one of the major indicators of development. There were enough studies on pre pregnancy body mass index (IPBMI) and inadequate weight gain during pregnancy (IWGP) of women in other part of the world and India, but none in Assam. In Assam a large number of population are in the category of low socio-economic group, a group most vulnerable to under nutrition. Thus this study was framed with the said indicators to throw light on the factors affecting the health status of pregnant women to accordingly address the situation. A cross sectional study using multistage sampling design with probability proportional to size was made comprising of 461 pregnant women belonging to low socio-economic status. Responses regarding their socio-economic, socio-cultural, health, diet and environmental background were collected and coded. The study revealed that although IPBMI (34.06%) was slightly lower than the reported state, national and global percentage the revealed IWGP (82%) was an astounding figure. The blood samples analyzed showed a high degree of inadequacy in almost all micronutrients (iron 63.1%, calcium 49.5% and copper 39.9%) studied in our survey.

Evaluation of Female Breast Cancer Risk Among the Betel Quid Chewer: A Bio-Statistical Assessment in Assam, India.

BACKGROUND: Breast cancer is the most commonly diagnosed cancer among the female population of Assam, India. Chewing of betel quid with or without tobacco is common practice among female population of this region. Moreoverthe method of preparing the betel quid is different from other parts of the country.So matched case control study is conducted to analyse whetherbetel quid chewing plays a significant role in the high incidence of breast cancer occurrences in Assam. METHODS AND MATERIAL: Here, controls are matched to the cases by age at diagnosis (±5 years), family income and place of residence with matching ratio 1:1. Conditional logistic regression models and odd ratios (OR) was used to draw conclusions. RESULTS: It is observed that cases are more habituated to chewing habits than the controls.Further the conditional logistic regression analysis reveals that betel quid chewer faces 2.353 times more risk having breast cancer than the non-chewer with p value 0.0003 (95% CI 1.334-4.150). CONCLUSION: Though the female population in Assam usually does not smoke, the addictive habits typical to this region have equal effect on the occurrence of breast cancer.

Nutritional status and the impact of socioeconomic factors on pregnant women in Kamrup district of Assam.

Pregnancy is a critical time in the course of life, having both health and social impacts for individuals, family, and society. The prevalence of undernutrition among pregnant women in a rural area of Assam, India, was examined using anthropometric and biochemical assessments. Key socioeconomic factors that affect nutritional status were examined. A cross-sectional study with a sample of 285 women from all three trimesters was done. The results found that 48% of the women were below normal for Body Mass Index (BMI), indicating a high level of undernutrition. The age of the mother and husband's occupation showed a strong positive correlation with BMI, while family size and income level showed a negative correlation. The results of the biochemical analysis showed that 62% of the women were anemic, and copper and zinc levels were 29% and 12% below normal levels, respectively. The study findings indicate that undernutrition is far higher than national and global standards.