Image Dataset on Eye Diseases Classification (Uveitis, Conjunctivitis, Cataract, Eyelid) with Symptoms and SMOTE Validation

Dataset Description: This dataset contains images and corresponding symptom descriptions for five types of eye diseases: Uveitis, Conjunctivitis, Cataract, Eyelid Drooping, and Normal. The dataset is intended for use in medical image analysis and machine learning model development. It includes image data (.jpg format) along with detailed descriptions of the diseases and their symptoms. Diseases Included: Normal: No abnormalities, clear vision, no redness or swelling. Uveitis: Eye redness, pain, blurred vision, sensitivity to light, and floating spots. Conjunctivitis: Redness, itching, tearing, discharge, and crusting of the eyelids. Cataract: Cloudy or blurred vision, difficulty seeing at night, sensitivity to glare. Eyelid Drooping: Drooping eyelids, swelling, irritation, and lumps on more than one eyelid. Symptoms: Each disease is associated with its symptoms as listed above. These symptoms are designed to help researchers and practitioners in the classification and diagnosis of these diseases based on visual and textual information. All the symptoms and dataset has been checked and corrected from a Professor from Bangladesh Eye Hospital. Data Collection: Images: The images were collected from online sources (via Google search) using disease-specific keywords. Data Quality Control: Duplicate images were removed, and the dataset was carefully reviewed for accuracy. Balanced Dataset: To ensure a balanced distribution of images across all diseases, SMOTE (Synthetic Minority Over-sampling Technique) was applied. The final dataset includes an equal number of images for each disease category (649 images per disease). Dataset Validation: Before SMOTE: Cataract: 544 images Conjunctivitis: 357 images Eyelid Drooping: 525 images Normal: 649 images Uveitis: 223 images After SMOTE: All diseases now have 649 images, resulting in a balanced dataset. The use of SMOTE ensures that all disease categories are equally represented, mitigating the risk of model bias due to class imbalance. File Formats: Images: JPEG format (.jpg) Dataset Usage: This dataset is intended for research purposes, specifically in medical image classification tasks. It can be used to train, test, and validate machine learning models that aim to identify and diagnose eye diseases based on images and symptoms. Ethics & Consent: The images used in this dataset have been gathered from publicly available online sources, and appropriate ethical considerations were taken into account. The dataset has been anonymized, and no personally identifiable information is included.数据集说明： 本数据集包含五种眼部疾病的图像及对应症状描述，分别为葡萄膜炎(Uveitis)、结膜炎(Conjunctivitis)、白内障(Cataract)、眼睑下垂(Eyelid Drooping)以及正常组(Normal)。本数据集旨在用于医学图像分析与机器学习模型开发，涵盖JPEG(.jpg)格式的图像数据，以及各疾病及其症状的详细描述文本。 纳入疾病类型： 正常组：无异常表现，视力清晰，无眼部红肿或肿胀。 葡萄膜炎(Uveitis)：眼部发红、疼痛、视力模糊、畏光及眼前漂浮黑影。 结膜炎(Conjunctivitis)：眼部发红、瘙痒、流泪、分泌物增多及眼睑结痂。 白内障(Cataract)：视物浑浊或模糊、夜间视力下降、畏强光。 眼睑下垂(Eyelid Drooping)：眼睑下垂、肿胀、眼部刺激及多眼睑出现肿块。 症状说明： 每种疾病均对应上述所列症状，这些症状可辅助研究人员与临床从业者基于视觉与文本信息对上述疾病进行分类与诊断。本数据集及所有症状均经孟加拉国眼科医院的一位教授审核校正。 数据采集： 图像通过疾病专属关键词，从在线资源（谷歌搜索）中获取。 数据质量管控： 已剔除重复图像，并对数据集进行了严格的准确性审核。 数据集均衡处理： 为确保各疾病类别间的图像分布均衡，本数据集采用合成少数类过采样技术(Synthetic Minority Over-sampling Technique, SMOTE)进行处理。最终数据集的每个疾病类别均包含等量图像（每类649张）。 数据集验证： SMOTE应用前： 白内障(Cataract)：544张 结膜炎(Conjunctivitis)：357张 眼睑下垂(Eyelid Drooping)：525张 正常组(Normal)：649张 葡萄膜炎(Uveitis)：223张 SMOTE处理后： 所有疾病类别均包含649张图像，数据集实现均衡分布。 采用该技术可确保所有疾病类别样本量均等，降低因类别不平衡导致的模型偏倚风险。 文件格式： 图像采用JPEG格式（.jpg）。 数据集用途： 本数据集仅供研究使用，专门面向医学图像分类任务，可用于基于图像与症状信息识别、诊断眼部疾病的机器学习模型的训练、测试与验证。 伦理与知情同意： 本数据集所用图像均来自公开在线资源，已充分考量相关伦理规范。数据集已完成匿名化处理，不包含任何个人可识别信息。