Computer Vision

Project aimed at training a deep learning model using Convolutional Neural Networks (CNN) for high-accuracy (over 99%) blood cancer detection, utilizing a large dataset of blood cell images.

Blood cell cancer detection by pytorch

Detecting white blood cancer cells based on the collected images by pattern recognition and machine learning techniques.

One of the most complex internal biological structures in the human body is liver .Upper right hand part of the abdomen is located by the liver which is reddish brown in colour and measures eight and half inches . Liver is wedge shaped gland normally weighs 1440grams to 1660 grams. Liver is divided into Left lobe and right lobe and filters 1.5L of blood per minute approximately.Liver cancer is the most dangerous cancer among variety of cancer. Due to this every third living is cause of death and which is nearly a sixth most common cancer in the world. Liver cancer is also known by the name hepatic cancer and most of the liver cancer is common to Hepatic cellular carcinoma (HCC). Liver cancer is the uncontrolled growing of tissue within the liver. Tumours are of two types such as non-cancerous cells (benign) and cancerous cells (malignant). There are 12000 deaths per year in world due to liver cancer. To avoid this, problem need to be analysed in earlier stages because earlier detection can help doctors to save lives and does not make very much complication on the human health. There are various techniques to acquire the image of liver from the patients those are Computed Tomography (CT), Magnetic Resonance Imaging (MRI) and Ultra Sounds but CT image is represented as accurate liver cancer diagnosis imaging modularity.With this in mind, ML algorithm has been used in many studies to predict the therapeutic outcome of HCC patients. Thus, in this review, the advantages and disadvantages of each ML algorithm are clarified, and relevant literature on the prediction of therapeutic outcomes after various treatment modalities for HCC is described.

Blood cell cancer detection using deep learning - 98.97% accuracy

a deep learning framework for early cancer detection using T cell receptor repertoire in peripheral blood

Blood cancer is an uprising issue and doing physical medical procedures is too sensitive and time-consuming to detect any blast cell. Manual testing includes blood tests, spinal fluid tests, bone marrow tests, imaging tests, etc. A solution to this is to use modern methods in health care that help to detect diseases faster and increase the cure rate.ssing and Transfer Learning for Detection of Types of Leukemia: In image processing, data preparation and image preprocessing are done where we have rescaled the image and adjusted the brightness to improve the image quality. Data augmentation is performed to increase the image count by flipping it horizontally and vertically. Images are converted to grayscale to reduce the matrix calculation.The images in the dataset are: AML has 935 images, ALL has 858, CML has 623 and CLL has 510. Transfer learning is used. I have used different pre-trained CNN models such as ResNet-50, VGG16, Inception V3, and MobileNet for feature extraction and classification.VGG16, InceptionV3 and MobileNet - all three models give 100% accuracy, while ResNet50 gives 85% accuracy.

Cloud-based blood cancer cell detection platform with secure image analysis, role-based dashboards, and CNN model integration.

A disease is a particular abnormal condition that negatively affects the structure or function of all or part of an organism, and that is not due to any immediate external injury. In humans, disease is often used more broadly to refer to any condition that causes pain, dysfunction, distress, social problems, or death to the person afflicted, or similar problems for those in contact with the person. Diseases such as Cancer is one which is having high mortality rate. Cancer is a disease in which some of the body’s cells grow uncontrollably and spread to other parts of the body causing death. Cancer is very hard to predict and if not predicted on time can be fatal. The technology today is very advanced and fast. Most cancer diseases like breast cancer can be predicted. Yet there are more of cancers which cannot be predicted at an early stage or till 3rd or 4th stage of disease, Such as Lung cancer, Colon Cancer, Leukaemia (Blood Cancer). Here in our project, we are trying to analyse Leukemia a patient is having by using a medical image. In our project “Leukemia Detection using Inception V3” we will be using microscopic images of WBC of the patients having Leukaemia of ALL type. With the help of DL methodologies, we will analyse the images and use them to train our model which will eventually predict whether the patient is having ALL, when a new image is given as input to the model. The scope of our project is very general and only for research purpose. Till now we are just focusing of how to use Deep Learning techniques to analyses an image and further train a model using images to predict leukemia. On a long-term perspective, we will surely try to take this project to next level where cancer will be predicted on early stage and in real time.

Blood Cancer Cell Detection for Laboratories

Deep learning project for blood cell classification using CNN and medical image datasets.

No description available

A project about detection of cancer in white blood cells using Convolutional Neural Network

A deep learning project for automated detection and classification of blood cells, with a focus on identifying abnormal and cancer-related cells using convolutional neural networks (CNN).

CNN model to classify blood cell images into 5 types (basophil, erythroblast, monocyte, myeloblast, seg_neutrophil) to assist in early detection of hematologic cancers.

Leukemia blood cancer is very dangerous among all types of cancers. As we all know early detection of cancer plays a main role in treatment of cancer. There are many different types of complex test to detect leukemia which takes time and depends on the experience of lab worker but by using Image processing the accuracy of test will increase and it take less time than other complex test. This project shows a better way to analysis and detection of cancer cell from blood by using patient blood cell microscopic Image. This method is using image preprocessing, image segmentation for segmenting different blood cells, edge detection for find the boundary shape and size of cells and finally clustering and classifiers for final output or result.

A deep learning-based system for the detection and classification of Acute Lymphoblastic Leukemia using microscopic blood cell images. This project uses the Blood Cell Cancer 4-Class dataset , which contains 3,256 images across four categories — Benign, Early, Pre, and Pro — representing different stages of ALL and non-cancerous hematogones.

Blood Cell Cancer Detection

white blood cancer cells detection

This project presents a deep learning-based system for blood cell cancer detection, specifically Acute Lymphoblastic Leukemia (ALL), using Inception V1 and ResNet 152. It employs data augmentation for better performance, with ResNet 152 outperforming Inception V1 with 99.59% accuracy.

Cancer_Cell_Detection_In_Blood_Sample

Bloods Cell Images for Cancer detection

No description available

No description available

No description available

No description available

No description available

No description available

No description available