Mammographic Images Research Articles

Avrupa Meme Görüntüleme Diploması Çoktan Seçmeli Örnek Soruları: Büyük Dil Modellerinin Yeteneklerinin Değerlendirilmesi

Dear editor, In pursuit of a fresh outlook, my objective was to gauge the abilities of a multimodal large language model (LLM) against sample questions from the European Diploma in Breast Imaging (EDBI) test, an initiative by the European Society of Breast Imaging. Large language models are pushing the potential in radiology, from interpreting text and medical images to generating reports (Bhayana, 2024). Generative Pre-trained Transformer 4 (GPT-4) has notably passed a national mammography board exam with clarity (Almeida et al, 2024). As the latest version among multimodal LLM types, GPT-4 is capable of answering questions requiring both lower-order and higher-order thinking. Three written sample questions, where multiple choices could be correct, were evaluated. It was noted that there was no negative marking for incorrect answers (https://www.eusobi.org/european-diploma-in-breast-imaging-edbi/). The scoring system was adapted from the European Diploma in Radiology scoring guidelines (https://www.myebr.org/edir-scoring-faqs). Data was obtained from Google Gemini, GPT-3.5, and GPT-4 in March 2024. When assigning a value of 1 point to each question, GPT-4 reached an accuracy of 78%, GPT-3.5 achieved 50%, and Google Gemini scored 22.2%. This notable success in the sample questions from the EDBI particularly emphasizes GPT-4's potential in aiding clinical decision-making. Future studies may assess its performance in questions requiring medical image analysis, such as mammography, breast ultrasound, or breast magnetic resonance imaging.

The Combination of Feature Extraction and Classification by Bag of Visual Words to Detect Breast Cancer for Improved Accuracy

The main objective is to improve diagnostic accuracy of the breast cancer. The goal is to improve the accuracy of breast cancer detection by leveraging both the discriminative power of feature extraction and the robustness of classification using BoVW. By combining these techniques, the system aims to effectively distinguish between cancerous and non-cancerous tissues in medical images, thereby aiding in early diagnosis and treatment planning for breast cancer patient. The breast cancer sample images were gathered from the city's hospitals Chennai 2023 as well as the mini-MIAS database and DDSM database. The BoVW classifier is used to identify and classify breast tumor images according to their size. This improves the efficacy of the classification method developed for the CAD system's mammography breast cancer picture categorization. Several Computer Aided Diagnosis (CAD) systems have been created to use mammography images to identify breast cancer in its early stages. ANN attained 93.6%,94.18%, 93.2% and SVM gained 92%,92.44%,93.02% for Precision, Recall and Accuracy Respectively.The proposed method display excellent accuracy of 98.83% over the other methods. The novelty could stem from the seamless integration of multiple modalities within the feature extraction and classification framework.

Unlocking the Potential Thorough Analysis of Machine Learning for Breast Cancer Diagnosis

To forecast breast cancer with the goal of giving a thorough rundown of current developments in the area. Given that breast cancer is among the world's leading causes of mortality for women; improving patient outcomes requires early detection. This study looks into the ability to predict outcomes using a variety of machine learning (ML) models, including random forests, logistic regression, support vector machines, decision trees, k- nearest neighbours, and deep learning neural networks, in predicting the incidence of breast cancer from patient data, including genetic markers, imaging results, and demographics. Aims to provide a comprehensive analysis of presetn advancements, obstacles, and prospects in the field of CNN-based techniques for breast cancer identification. The review begins by outlining the urgent need for reliable and accurate diagnostic methods for breast cancer, highlighting the critical role that early identification plays in enhancing patient outcomes. Which delves into the intricate architecture of CNNs, revealing its unique applicability to mammography image analysis as well as their innate advantages in image classification tasks. Important topics of discussion include the various CNN architectures used for two- and three- dimensional (2D) imaging methods used in breast cancer diagnosis.

Deep Learning in Breast Cancer Imaging: State of the Art and Recent Advancements in Early 2024.

The rapid advancement of artificial intelligence (AI) has significantly impacted various aspects of healthcare, particularly in the medical imaging field. This review focuses on recent developments in the application of deep learning (DL) techniques to breast cancer imaging. DL models, a subset of AI algorithms inspired by human brain architecture, have demonstrated remarkable success in analyzing complex medical images, enhancing diagnostic precision, and streamlining workflows. DL models have been applied to breast cancer diagnosis via mammography, ultrasonography, and magnetic resonance imaging. Furthermore, DL-based radiomic approaches may play a role in breast cancer risk assessment, prognosis prediction, and therapeutic response monitoring. Nevertheless, several challenges have limited the widespread adoption of AI techniques in clinical practice, emphasizing the importance of rigorous validation, interpretability, and technical considerations when implementing DL solutions. By examining fundamental concepts in DL techniques applied to medical imaging and synthesizing the latest advancements and trends, this narrative review aims to provide valuable and up-to-date insights for radiologists seeking to harness the power of AI in breast cancer care.

Attention-map augmentation for hypercomplex breast cancer classification

Breast cancer is the most widespread neoplasm among women and early detection of this disease is critical. Deep learning techniques have become of great interest to improve diagnostic performance. However, distinguishing between malignant and benign masses in whole mammograms poses a challenge, as they appear nearly identical to an untrained eye, and the region of interest (ROI) constitutes only a small fraction of the entire image. In this paper, we propose a framework, parameterized hypercomplex attention maps (PHAM), to overcome these problems. Specifically, we deploy an augmentation step based on computing attention maps. Then, the attention maps are used to condition the classification step by constructing a multi-dimensional input comprised of the original breast cancer image and the corresponding attention map. In this step, a parameterized hypercomplex neural network (PHNN) is employed to perform breast cancer classification. The framework offers two main advantages. First, attention maps provide critical information regarding the ROI and allow the neural model to concentrate on it. Second, the hypercomplex architecture has the ability to model local relations between input dimensions thanks to hypercomplex algebra rules, thus properly exploiting the information provided by the attention map. We demonstrate the efficacy of the proposed framework on both mammography images as well as histopathological ones. We surpass attention-based state-of-the-art networks and the real-valued counterpart of our approach. The code of our work is available at https://github.com/ispamm/AttentionBCS.

Investigating the detection of breast cancer with deep transfer learning using ResNet18 and ResNet34

A lot of underdeveloped nations particularly in Africa struggle with cancer-related, deadly diseases. Particularly in women, the incidence of breast cancer is rising daily because of ignorance and delayed diagnosis. Only by correctly identifying and diagnosing cancer in its very early stages of development can be effectively treated. The classification of cancer can be accelerated and automated with the aid of computer-aided diagnosis and medical image analysis techniques. This research provides the use of transfer learning from a Residual Network 18 (ResNet18) and Residual Network 34 (ResNet34) architectures to detect breast cancer. The study examined how breast cancer can be identified in breast mammography pictures using transfer learning from ResNet18 and ResNet34, and developed a demo app for radiologists using the trained models with the best validation accuracy. 1, 200 datasets of breast x-ray mammography images from the National Radiological Society’s (NRS) archives were employed in the study. The dataset was categorised as implant cancer negative, implant cancer positive, cancer negative and cancer positive in order to increase the consistency of x-ray mammography images classification and produce better features. For the multi-class classification of the images, the study gave an average accuracy for binary classification of benign or malignant cancer cases of 86.7% validation accuracy for ResNet34 and 92% validation accuracy for ResNet18. A prototype web application showcasing ResNet18 performance has been created. The acquired results show how transfer learning can improve the accuracy of breast cancer detection, providing invaluable assistance to medical professionals, particularly in an African scenario.

A Novel Structure Fusion Attention Model to Detect Architectural Distortion on Mammography.

Architectural distortion (AD) is one of the most common findings on mammograms, and it may represent not only cancer but also a lesion such as a radial scar that may have an associated cancer. AD accounts for 18-45% missed cancer, and the positive predictive value of AD is approximately 74.5%. Early detection of AD leads to early diagnosis and treatment of the cancer and improves the overall prognosis. However, detection of AD is a challenging task. In this work, we propose a new approach for detecting architectural distortion in mammography images by combining preprocessing methods and a novel structure fusion attention model. The proposed structure-focused weighted orientation preprocessing method is composed of the original image, the architecture enhancement map, and the weighted orientation map, highlighting suspicious AD locations. The proposed structure fusion attention model captures the information from different channels and outperforms other models in terms of false positives and top sensitivity, which refers to the maximum sensitivity that a model can achieve under the acceptance of the highest number of false positives, reaching 0.92 top sensitivity with only 0.6590 false positive per image. The findings suggest that the combination of preprocessing methods and a novel network architecture can lead to more accurate and reliable AD detection. Overall, the proposed approach offers a novel perspective on detecting ADs, and we believe that our method can be applied to clinical settings in the future, assisting radiologists in the early detection of ADs from mammography, ultimately leading to early treatment of breast cancer patients.

Calcification-associated molecular traits and therapeutic strategies in hormone receptor-positive HER2-negative breast cancer.

Mammographic calcifications are a common feature of breast cancer, but their molecular characteristics and treatment implications in hormone receptor-positive (HR+)/human epidermal growth factor receptor 2-negative (HER2-) breast cancer remain unclear. We retrospectively collected mammography records of an HR+/HER2- breast cancer cohort (n = 316) with matched clinicopathological, genomic, transcriptomic, and metabolomic data. On the basis of mammographic images, we grouped tumors by calcification status into calcification-negative tumors, tumors with probably benign calcifications, tumors with calcification of low-moderate suspicion for maligancy and tumors with calcification of high suspicion for maligancy. We then explored the molecular characteristics associated with each calcification status across multiple dimensions. Among the different statuses, tumors with probably benign calcifications exhibited elevated hormone receptor immunohistochemical staining scores, estrogen receptor (ER) pathway activation, lipid metabolism, and sensitivity to endocrine therapy. Tumors with calcifications of high suspicion for malignancy had relatively larger tumor sizes, elevated lymph node metastasis incidence, Ki-67 staining scores, genomic instability, cell cycle pathway activation, and may benefit from cyclin-dependent kinase 4 and 6 (CDK4/6) inhibitors. Our research established links between tumor calcifications and molecular features, thus proposing potential precision treatment strategies for HR+/HER2- breast cancer.

Evaluation of Multimodal ChatGPT (GPT-4V) in Describing Mammography Image Features.

Evaluation of Multimodal ChatGPT (GPT-4V) in Describing Mammography Image Features.

Application of deep learning on mammographies to discriminate between low and high-risk DCIS for patient participation in active surveillance trials

BackgroundDuctal Carcinoma In Situ (DCIS) can progress to invasive breast cancer, but most DCIS lesions never will. Therefore, four clinical trials (COMET, LORIS, LORETTA, AND LORD) test whether active surveillance for women with low-risk Ductal carcinoma In Situ is safe (E. S. Hwang et al., BMJ Open, 9: e026797, 2019, A. Francis et al., Eur J Cancer. 51: 2296–2303, 2015, Chizuko Kanbayashi et al. The international collaboration of active surveillance trials for low-risk DCIS (LORIS, LORD, COMET, LORETTA), L. E. Elshof et al., Eur J Cancer, 51, 1497–510, 2015). Low-risk is defined as grade I or II DCIS. Because DCIS grade is a major eligibility criteria in these trials, it would be very helpful to assess DCIS grade on mammography, informed by grade assessed on DCIS histopathology in pre-surgery biopsies, since surgery will not be performed on a significant number of patients participating in these trials.ObjectiveTo assess the performance and clinical utility of a convolutional neural network (CNN) in discriminating high-risk (grade III) DCIS and/or Invasive Breast Cancer (IBC) from low-risk (grade I/II) DCIS based on mammographic features. We explored whether the CNN could be used as a decision support tool, from excluding high-risk patients for active surveillance.MethodsIn this single centre retrospective study, 464 patients diagnosed with DCIS based on pre-surgery biopsy between 2000 and 2014 were included. The collection of mammography images was partitioned on a patient-level into two subsets, one for training containing 80% of cases (371 cases, 681 images) and 20% (93 cases, 173 images) for testing. A deep learning model based on the U-Net CNN was trained and validated on 681 two-dimensional mammograms. Classification performance was assessed with the Area Under the Curve (AUC) receiver operating characteristic and predictive values on the test set for predicting high risk DCIS-and high-risk DCIS and/ or IBC from low-risk DCIS.ResultsWhen classifying DCIS as high-risk, the deep learning network achieved a Positive Predictive Value (PPV) of 0.40, Negative Predictive Value (NPV) of 0.91 and an AUC of 0.72 on the test dataset. For distinguishing high-risk and/or upstaged DCIS (occult invasive breast cancer) from low-risk DCIS a PPV of 0.80, a NPV of 0.84 and an AUC of 0.76 were achieved.ConclusionFor both scenarios (DCIS grade I/II vs. III, DCIS grade I/II vs. III and/or IBC) AUCs were high, 0.72 and 0.76, respectively, concluding that our convolutional neural network can discriminate low-grade from high-grade DCIS.

AN AUTOMATED BREAST CANCER DETECTION BY HEURISTIC-BASED ENSEMBLE DEEP CLASSIFIER USING MAMMOGRAM AND TOMOSYNTHESIS IMAGES

Nowadays, breast cancer is the most founded cancer among women. Moreover, 2.3 million new cases of breast cancer have been detected among women since 2020 as reported by World Health Organization (WHO). Many research studies based on breast cancer mainly aim at ultrasound, mammography, and Magnetic Resonance Images (MRI). However, there exist certain limitations such as lack of access for the detection of disease in rural and remote cities and insufficient knowledge about the availability of computer-aided systems. In order to overcome the problem, it is necessary to develop an effective detection model for breast cancer. For the experimentation, the input images like mammogram images and tomosynthesis images are garnered from the benchmark online resources. The input image further undergoes the pre-processing stage, which is made by using “Contrast Limited Adaptive Histogram Equalization (CLAHE) and histogram equalization (HE)”. Then, the pre-processed images are further given as input to the segmentation, in which deeplabv3 is deployed. Consequently, with the assistance of segmented images, the relevant features like “texture features, color features, shape features, deep features, statistical features and morphological features” are extracted. Then, these obtained features are used in weighted feature selection, where the optimal feature selection is performed, and weights are optimized by the Enhanced Adaptive Prey Location-Based Pelican Optimization Algorithm (APL-POA). Finally, the weighted accurate features are given as input to the Ensemble Deep Learning (EDC) model. The ensemble model is structured by “Deep Neural Network (DNN), Recurrent Neural Network (RNN), Bidirectional Long Short-Term Memory (Bi-LSTM), Deep Temporal Convolution Networks (DTCN) and Gated Recurrent Unit (GRU)”, in which the hyperparameters of every classifier are smoothened optimally by the enhanced APL-POA algorithm. Through the experimental analysis, the proposed work tends to provide an improved classification rate and rapid detection of disease that aids in better diagnosis of the patients.

Study on the differential diagnosis of benign and malignant breast lesions using a deep learning model based on multimodal images.

To establish a multimodal model for distinguishing benign and malignant breast lesions. Clinical data, mammography, and MRI images (including T2WI, diffusion-weighted images (DWI), apparent diffusion coefficient (ADC), and DCE-MRI images) of 132 benign and breast cancer patients were analyzed retrospectively. The region of interest (ROI) in each image was marked and segmented using MATLAB software. The mammography, T2WI, DWI, ADC, and DCE-MRI models based on the ResNet34 network were trained. Using an integrated learning method, the five models were used as a basic model, and voting methods were used to construct a multimodal model. The dataset was divided into a training set and a prediction set. The accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of the model were calculated. The diagnostic efficacy of each model was analyzed using a receiver operating characteristic curve (ROC) and an area under the curve (AUC). The diagnostic value was determined by the DeLong test with statistically significant differences set at P < 0.05. We evaluated the ability of the model to classify benign and malignant tumors using the test set. The AUC values of the multimodal model, mammography model, T2WI model, DWI model, ADC model and DCE-MRI model were 0.943, 0.645, 0.595, 0.905, 0.900, and 0.865, respectively. The diagnostic ability of the multimodal model was significantly higher compared with that of the mammography and T2WI models. However, compared with the DWI, ADC, and DCE-MRI models, there was no significant difference in the diagnostic ability of these models. Our deep learning model based on multimodal image training has practical value for the diagnosis of benign and malignant breast lesions.

Correlation Analysis of Digital Mammography, Ultrasonography, and Pathologic Features in Pure Invasive Micropapillary Carcinoma of the Breast (PIMPC).

This study determined the digital mammography and ultrasonography imaging features of pure invasive micropapillary carcinoma of the breast (PIMPC) and the correlation with pathologic features. Nineteen patients diagnosed with PIMPC at Yantaishan Hospital from October 2015 to February 2022 were included in the study group. Forty patients with breast masses diagnosed as nonspecific invasive ductal carcinoma of the breast (NIDC) from July to December 2021 were included in the control group. Digital mammography and ultrasonography features were compared between the two groups. Patients with PIMPC had a younger age profile compared to patients with NIDC (P=0.017). Moreover, PIMPC masses were smaller than NIDC masses (P=0.040). Imaging features analysis revealed significant differences in age groups (<45 years: χ²=5.971, P=0.044) and the presence of spiculations or the crab claw sign (χ²=8.583, P=0.004) between patients with PIMPC and NIDC. However, there were no statistically significant differences in the presence of calcifications, blood flow grading, pathologic molecular subtypes between the study and control groups. The Ki-67 proliferative index (χ²=1.052, P=0.389), vascular invasion (χ²=2.263, P=0.197), and lymph node metastasis (χ²=1.968, P=0.386) showed no significant differences between PIMPC and NIDC patients. PIMPC imaging features show specificity, such as tiny breast masses, spiculated edges, or crab claw-like patterns, and malignant signs appeared when the lesion was <2 cm in diameter. PIMPC mainly occurs in middle-aged women 45-59 y of age. Patients with PIMPC and NIDC of the breast are frequently associated with lymph node metastases and greater than one-half of the cases (74%) were shown to have a Ki-67 index >30%, suggesting a significant risk of recurrence and metastasis. Early therapeutic care for these patients is crucial. These results warrant further validation with additional samples from several centers due to the limited single-center sample size in the current study.

The application of mammography imaging in the diagnosis and prediction of breast diseases

Breast diseases pose a significant threat to women’s health, so early detection and treatment are extremely important. In this context, early disease identification has become crucial in the diagnosis and treatment of breast diseases. This paper begins by outlining the pivotal role of mammography in the early diagnosis of breast cancer, comparing the structural similarities and differences between normal and diseased breast tissues. This comparison underscores the primary role of mammography in the diagnosis and treatment of breast diseases. Additionally, our paper provides an overview of fundamental concepts related to breast cancer detection, diagnosis, and prediction systems. It delves into the latest research developments in auxiliary diagnostic detection, examination, and risk prediction systems associated with breast cancer. Our objective is to offer a comprehensive understanding of the role of computer-aided detection, diagnosis, and prediction systems in breast diseases, fostering further development and application. This work aims to explore and drive innovation in the field, enhance early detection rates of breast diseases, and guide readers towards novel directions, thus contributing to female healthcare management.

Breast Cancer Screening and Prophylactic Mastectomy for High-Risk Women in Romania.

Breast cancer remains a significant contributor to morbidity and mortality within oncology. Risk factors, encompassing genetic and environmental influences, significantly contribute to its prevalence. While germline mutations, notably within the BRCA genes, are commonly associated with heightened breast cancer risk, a spectrum of other variants exists among affected individuals. Diagnosis relies on imaging techniques, biopsies, biomarkers, and genetic testing, facilitating personalised risk assessment through specific scoring systems. Breast cancer screening programs employing mammography and other imaging modalities play a crucial role in early detection and management, leading to improved outcomes for affected individuals. Regular screening enables the identification of suspicious lesions or abnormalities at earlier stages, facilitating timely intervention and potentially reducing mortality rates associated with breast cancer. Genetic mutations guide screening protocols, prophylactic interventions, treatment modalities, and patient prognosis. Prophylactic measures encompass a range of interventions, including chemoprevention, hormonal inhibition, oophorectomy, and mastectomy. Despite their efficacy in mitigating breast cancer incidence, these interventions carry potential side effects and psychological implications, necessitating comprehensive counselling tailored to individual cases.

Arterial Calcification Disappearance in Breast Imaging: A Key Indicator for Transition to Invasive Ductal Carcinoma.

A woman in her 70s, initially suspected of having fibroadenoma due to a well-defined mass in her breast, underwent regular mammography and ultrasound screenings. Over several years, no appreciable alterations in the mass were observed, maintaining the fibroadenoma diagnosis. However, in the fourth year, an ultrasound indicated slight enlargement and peripheral irregularities in the mass, even though the mammography images at that time showed no alterations. Interestingly, mammography images over time showed the gradual disappearance of previously observed arterial calcification around the mass. Pathological examination eventually identified the mass as invasive ductal carcinoma. Although the patient had breast tissue arterial calcification typical of atherosclerosis, none was present around the tumor-associated arteries. This case highlights the importance of monitoring arterial calcification changes in mammography, suggesting that they are crucial indicators in breast cancer diagnosis, beyond observing size and shape alterations.

Optimized signal of calcifications in wide-angle digital breast tomosynthesis: a virtual imaging trial.

Evaluate microcalcification detectability in digital breast tomosynthesis (DBT) and synthetic 2D mammography (SM) for different acquisition setups using a virtual imaging trial (VIT) approach. Medio-lateral oblique (MLO) DBT acquisitions on eight patients were performed at twice the automatic exposure controlled (AEC) dose. The noise was added to the projections to simulate a given dose trajectory. Virtual microcalcification models were added to a given projection set using an in-house VIT framework. Three setups were evaluated: (1) standard acquisition with 25 projections at AEC dose, (2) 25 projections with a convex dose distribution, and (3) sparse setup with 13 projections, every second one over the angular range. The total scan dose and angular range remained constant. DBT volume reconstruction and synthetic mammography image generation were performed using a Siemens prototype algorithm. Lesion detectability was assessed through a Jackknife-alternative free-response receiver operating characteristic (JAFROC) study with six observers. For DBT, the area under the curve (AUC) was 0.97 ± 0.01 for the standard, 0.95 ± 0.02 for the convex, and 0.89 ± 0.03 for the sparse setup. There was no significant difference between standard and convex dose distributions (p = 0.309). Sparse projections significantly reduced detectability (p = 0.001). Synthetic images had a higher AUC with the convex setup, though not significantly (p = 0.435). DBT required four times more reading time than synthetic mammography. A convex setup did not significantly improve detectability in DBT compared to the standard setup. Synthetic images exhibited a non-significant increase in detectability with the convex setup. Sparse setup significantly reduced detectability in both DBT and synthetic mammography. This virtual imaging trial study allowed the design and efficient testing of different dose distribution trajectories with real mammography images, using a dose-neutral protocol. • In DBT, a convex dose distribution did not increase the detectability of microcalcifications compared to the current standard setup but increased detectability for the SM images. • A sparse setup decreased microcalcification detectability in both DBT and SM images compared to the convex and current clinical setups. • Optimal microcalcification cluster detection in the system studied was achieved using either the standard or convex dose setting, with the default number of projections.

A Novel Transfer Learning Approach to Improve Breast Cancer Diagnosing on Screening Mammography

Segmentation is a technique for separating an image into discrete areas in order to separate objects of interest from their surroundings. In image analysis, segmentation—which encompasses detection, feature extraction, classification, and treatment—is crucial. In order to plan treatments, segmentation aids doctors in measuring the amount of tissue in the breast. Categorizing the input data into two groups that are mutually exclusive is the aim of a binary classification problem. In this case, the training data is labeled in a binary format based on the problem being solved. Identifying breast lumps accurately in mammography pictures is essential for the purpose of prenatal testing for breast cancer. The proposed TLA (Transfer Learning Approach) based CNN (Convolution Neural Network) –TLA based CNN aims to offer binary classification for rapid and precise breast cancer diagnosis (benign and malignant). In order to predict the sub-type of cancer, this exploration as used Deep Learning techniques on the Histogram of Oriented Gradient (HOG) - Feature extraction technique that creates a local histogram of the image to extract features from each place in the image with CNN classifier. This research work employs two well-known pre-trained models, ResNet-50 and VGG16, to extract characteristics from mammography images. The high-level features from the Mammogram dataset are extracted using a transfer learning model based on Visual Geometry Group (VGG) with 16-layer and Residual Neural Network with 50-layers deep model architecture (ResNet-50). The proposed model TLA based CNN has achieved 96.49% and 95.48% accuracy as compared to ResNet50 and VGG16 in the breast cancer classification and segmentation.

The Role of Traditional Medicines as Chemopreventive and Suppressive Agents in Cancer Treatment Deanova Insiratu

The purpose of this study was to determine the role of traditional medicine as a chemopreventive and suppressive agent in the treatment of Deanova Insiratu Cancer. This study is a qualitative study, this study will use a retrospective and prospective observational approach. Data will be collected from the medical records of patients who have used Deanova Insiratu previously, as well as from direct observation and interviews with individuals who are currently using or have experience with this traditional medicine. Research Results Cancer or malignant tumors can occur due to the growth of body tissue cells that proliferate abnormally. Cancer prevention can be done with healthy living habits and avoiding risk factors that can cause cancer. Cancer treatment can be done in 4 ways, namely by surgery, radiotherapy, immunotherapy and chemotherapy. Cancer screening can be done with mammography, ultrasonography (USG), and Magnetic Resonance Imaging (MRI). Natural ingredients that have effects as cancer prevention and help cancer treatment include Soursop leaves (Annona muricata), Celery leaves (Apium graveolans linn), Red Atai Spike leaves (Angiopteris ferox copel), Benalu Kepel (Dendrophthoe pentandra (L.) Miq.), and Tapak Dara leaves (Catharanthus roseus).

Mammo-Light: A lightweight convolutional neural network for diagnosing breast cancer from mammography images

People of all countries, developed and developing alike endure cancer-related fatal diseases. The rate of breast cancer in females is increasing daily, partly due to ignorance and misdiagnosis in the early stages. Diagnosis of breast cancer accurately during its earlier stages of development can result in proper initial treatment for breast cancer. Artificial intelligence can aid in the acceleration and automation of breast cancer detection. Deep learning is decisive in effectively recognizing and classifying cancer on large datasets of medical images. In this paper, we propose a novel computer-aided classification approach, Mammo-Light for breast cancer prediction. Preprocessing strategies have been utilized to eradicate the noise and enhance mammogram lesions. Photometric augmentation techniques adapted to the preprocessed classes to balance and increase the size of the dataset. After that, a lightweight yet intuitive convolutional neural network is applied to classify breast cancer on the publicly available dataset CBIS-DDSM. For further validation of the proposed approach, we have used the MIAS dataset. Mammo-Light attained a 99.17% and 98.42% test accuracy respectively for CBIS-DDSM and MIAS datasets and outperformed state-of-the-art methods in terms of accuracy and other metrics. Due to being the lightweight model, Mammo-Light performs exceptionally well with fewer parameters and computational time, which can potentially contribute to the field of breast cancer early diagnosis and enable fast treatment.